A large language model system prompt priority alignment benchmarking method

Abstract

The application discloses a large language model system prompt priority alignment benchmark test method, acquires a plurality of system prompts, constructs benchmark data including synthetic diagnosis branches and real prompt branches; extracts and normalizes the system prompts to obtain rule records including rule content and detection conditions; determines target rules based on preset failure types such as forgetting type, misunderstanding type and attack type, and generates corresponding user-side stress prompts to form test cases; inputs the system prompts and user-side stress prompts in the test cases into a large language model to be tested to obtain model responses; performs rule-by-rule compliance judgment on the model responses based on the rule records, and statistically obtains overall accuracy, rule-level accuracy and target accuracy, and calculates a level retention score; and determines a level degradation diagnosis result based on the level retention score before and after the first round of compliance filtering. The application can quantitatively evaluate the system prompt priority retention capability under user-side stress.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence model evaluation technology, and in particular to a benchmark testing method for prompt priority alignment in a large language model system. Background Technology

[0002] Large-scale language model applications typically organize interaction processes through different levels of prompts. System prompts are used to set global constraints such as role identity, output format, security boundaries, and tool invocation strategies, while user prompts express specific task requests. Ideally, when conflicts arise between system and user prompts, the model should prioritize the global constraints corresponding to the system prompts to ensure the stability and controllability of the model application in agent security, enterprise deployment, policy enforcement, and automated tasks. However, in practice, the model may deviate from system prompts due to context drift in multi-turn dialogues, over-compliance with user intent, misunderstanding of rule boundaries, or user-side overriding instructions, causing the priority of system prompts to fail to be consistently maintained.

[0003] Existing large-scale language model evaluation schemes cover areas such as general instruction compliance, system message compliance, multi-turn stability, and hint injection attack testing. However, these schemes typically focus on overall task completion or single-type attack pressure, making it difficult to differentiate the reasons for system hint priority failure in a fine-grained manner. While some evaluation methods can determine whether the model follows certain constraints, they often rely on pre-set synthesized instructions or overall pass rates, making it difficult to extract objective and detectable rules from real-world application system hints, and also difficult to pinpoint the specific target rule being violated. Other hint injection testing schemes mainly focus on malicious overwriting or privilege escalation attacks, with insufficient coverage of hierarchical degradation issues such as forgetting, misunderstanding, and condition relaxation in non-adversarial scenarios.

[0004] Therefore, existing technologies still need a benchmark testing method for maintaining the priority of system prompts, which can simultaneously cover synthetic diagnostic scenarios and real-world application prompt scenarios. Through rule extraction, target rule pressure generation, rule-by-rule compliance judgment, and hierarchical indicator statistics, a reproducible and interpretable quantitative evaluation of the model's ability to maintain hierarchy when system prompts and user-side pressure coexist can be achieved. Summary of the Invention

[0005] This application provides a benchmarking method for aligning prompt priorities in large language model systems, in order to address the problem in the prior art of making it difficult to perform interpretable and quantitative evaluation of the ability of large language model systems to maintain prompt priorities under user-side pressure.

[0006] To address the aforementioned issues, this application provides a benchmark testing method for prioritizing system prompts in a large-scale language model system. The method includes: acquiring multiple system prompts and constructing benchmark data comprising a synthetic diagnostic branch and a real prompt branch. The synthetic diagnostic branch generates deterministically verifiable system prompt conflict cases, while the real prompt branch generates rule records based on real-world application system prompts. Rules are extracted and normalized from each system prompt to obtain rule records, which include rule content and detection conditions. Target rules are determined from the rule records based on preset failure types, and corresponding user-side pressure prompts are generated according to the target rules and preset failure types to form test cases. Preset failure types include forgetting, misunderstanding, and attack types. The system prompts and user-side pressure prompts from the test cases are input into the large-scale language model under test to obtain model responses. Rule-by-rule compliance judgments are performed on the model responses based on the rule records, and case-level accuracy, rule-level accuracy, and target rule accuracy are statistically analyzed across multiple test cases. A hierarchy preservation score is calculated based on the case-level accuracy, rule-level accuracy, and target rule accuracy, and a hierarchy degradation diagnostic result is determined based on the hierarchy preservation score before and after the first round of compliance filtering.

[0007] Furthermore, the system prompt conflict cases that can be deterministically verified include at least one of the following: helpful tension cases, direct conflict cases, delayed multi-round conflict cases, and reasoning to answer compression cases; the detection conditions corresponding to the system prompt conflict cases include at least one of the following: exact matching conditions, quantity constraints, field constraints, and structured format constraints.

[0008] Furthermore, the rule records corresponding to the prompts from the real application system are generated in the following way: candidate rules are extracted from the prompts from the real application system, the candidate rules are filtered for suitability, the candidate rules that are suitable for the text generation task and have detection conditions are retained, and the retained candidate rules are determined as rule records.

[0009] Furthermore, rules are extracted and standardized from each system prompt to obtain rule records, including: splitting system prompts into multiple candidate constraints, deleting subjective constraints and purely descriptive constraints, retaining candidate constraints that can be judged for compliance through detection conditions, and generating rule content, detection conditions, rule source and rule identifier for the retained candidate constraints.

[0010] Furthermore, the forgetting category includes at least one of single-round drift, multi-round drift, and reasoning-to-answer drift; the misunderstanding category includes at least one of completion bias, role boundary expansion, partial compliance fallacy, readability coverage bias, and condition relaxation misunderstanding; the attack category includes at least one of explicit coverage, role reassignment, task restructuring, indirect execution, nested injection, reference injection, constraint trade-off, progressive inducement, authority coverage, and meta-justification coverage.

[0011] Furthermore, based on the target rules and preset failure types, corresponding user-side pressure prompts are generated, including: determining the prompt generation template corresponding to the preset failure type, using the rule content and detection conditions of the target rules as template conditions, and generating user-side pressure prompts that form explicit conflict, implicit conflict, semantic reinterpretation, or multi-round forgetting pressure with the target rules.

[0012] Furthermore, the model response is evaluated for compliance on a rule-by-rule basis based on the rule records, including: for each rule record in the same test case, it is determined whether the model response meets the corresponding detection conditions, the compliance result of each rule record is generated, and the compliance result of the target rule is determined based on the compliance result of the target rule.

[0013] Furthermore, case-level accuracy is the percentage of test cases in which all rule records are judged to be satisfied out of multiple test cases; rule-level accuracy is the percentage of rule records in which all rule records are judged to be satisfied out of all rule records; and target rule accuracy is the percentage of target rules in which all target rules are judged to be satisfied out of all target rules.

[0014] Furthermore, the hierarchical preservation score is obtained by weighted geometric mean of case-level accuracy, rule-level accuracy, and target rule accuracy, wherein the weights corresponding to case-level accuracy, rule-level accuracy, and target rule accuracy are all greater than zero and the sum of the weights is 1.

[0015] Furthermore, the hierarchical degradation diagnosis results are determined based on the hierarchical preservation scores before and after the first round of compliance filtering, including: filtering test cases where the responses generated by the large language model under test in the first round of user requests do not meet the rule records, recalculating the hierarchical preservation scores based on the filtered test cases, and determining the impact of basic instruction following failure and subsequent hierarchical degradation on the test results based on the difference in hierarchical preservation scores before and after filtering.

[0016] Compared with existing technologies, the above technical solution has at least the following beneficial effects: This application constructs benchmark data by combining synthetic diagnostic branches and real-world prompt branches, enabling the testing process to cover both deterministically verifiable system prompt conflict cases and generate rule records for real-world application system prompts, thereby avoiding evaluation bias caused by relying solely on synthetic data or a single scenario. By extracting and standardizing rules from system prompts and solidifying the rule content and detection conditions into rule records, abstract system prompt constraints can be transformed into judgmental and statistically significant evaluation objects. Furthermore, by determining target rules and generating user-side pressure prompts through preset failure types such as forgetting, misunderstanding, and attack, the degradation process of system prompt priority under conditions such as multi-round drift, semantic misunderstanding, and overlay attacks can be specifically simulated, thereby improving the benchmark test's coverage of real-world deployment risks and its problem localization capabilities.

[0017] The above-mentioned technical solutions also have the following advantages: After the model response is generated, this application performs rule-by-rule compliance judgment based on rule records, and statistically analyzes case-level accuracy, rule-level accuracy, and target rule accuracy on multiple test cases. This ensures that the evaluation results not only reflect whether the overall test cases pass, but also whether individual rules and target rules hit by pressure are maintained. Based on this, the hierarchical retention score is calculated using multiple types of accuracy, resulting in a comprehensive evaluation result that is easy to compare horizontally. By determining the hierarchical degradation diagnosis result through the hierarchical retention score before and after the first round of compliance filtering, it is possible to distinguish between insufficient initial instruction following capability of the model and system prompt priority degradation caused by subsequent interaction pressure. This improves the interpretability, reproducibility, and engineering application value of the evaluation results, facilitating reliability verification before agent systems, security policy execution, and enterprise-level model deployment. Attached Figure Description

[0018] Figure 1 Flowchart of the priority alignment benchmarking method for large language model systems provided in this application; Figure 2 A schematic diagram illustrating the construction of the baseline data for the synthetic diagnostic branch and the true indication branch provided in this application; Figure 3 A schematic diagram of the data structure for the rule records and test cases provided in this application; Figure 4 The flowchart for generating user-side pressure prompts and performing rule-by-rule compliance judgments provided in this application; Figure 5 The first-round compliance filtering diagnostic flowchart provided for this application; Figure 6 Cross-model comparison chart of the original metrics provided for this application and the first round of compliance filtering metrics; Figure 7 An exploratory accuracy difference plot showing the impact of chain reasoning on hierarchy preservation provided in this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and beneficial effects of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to explain the technical solutions of this application and are not intended to limit this application.

[0020] In the description of this application, the terms "first," "second," etc., are used only to distinguish different objects and should not be construed as indicating or implying relative importance. The term "multiple" means two or more. The terms "comprising," "including," "having," and their variations have an open meaning, meaning that in addition to the expressly listed steps, components, or data fields, other steps, components, or data fields may be included. The term "and / or" means any one or any combination of the listed objects.

[0021] In this application, a large language model (LLM) refers to a machine learning model capable of generating natural language text, structured text, or other parsable responses based on input prompts. System prompts refer to prompts used before a dialogue or task begins to set role identities, output formats, security boundaries, tool invocation strategies, or other global constraints. User prompts refer to specific requests entered by the user during the task. System prompt priority refers to the higher execution priority of the global constraints corresponding to system prompts compared to user prompts when there is conflict or tension between system prompts and user prompts. System prompt priority alignment refers to the ability of the large language model under test to maintain system prompt constraints under single-turn, multi-turn, inference compression, user-side coverage, or other stressful scenarios.

[0022] In this application, baseline data refers to the dataset used to test the system prompt priority preservation capability of the large language model under test. Rule records refer to data records obtained by extracting and normalizing the constraints in the system prompts. Rule records include at least rule content and detection conditions, and may also include fields such as rule identifier, rule source, target marker, and applicability label. Detection conditions refer to the conditions used to determine whether the model response meets the rule content, such as exact match conditions, quantity constraints, field constraints, and structured format constraints. Target rules refer to the rules selected as the stress object when generating user-side stress prompts. User-side stress prompts refer to user prompts generated based on target rules and preset failure types, used to test whether the large language model under test deviates from the system prompt constraints. Rule-by-rule compliance judgment refers to judging whether the model response meets each rule record individually, rather than just judging whether the entire model response passes. The hierarchy preservation score, or HPS for short, is a comprehensive evaluation index calculated based on case-level accuracy, rule-level accuracy, and target rule accuracy. The first-round compliance filtering diagnostic, also known as IF-Controlled diagnostic, refers to first filtering test cases that do not meet the rules recorded under the first round of user requests, and then recalculating the hierarchical preservation score to distinguish between basic instruction follow-up failure and subsequent hierarchical degradation.

[0023] Figure 1A flowchart illustrating the priority alignment benchmarking method for the large language model system provided in this application. Figure 1 As shown, the priority alignment benchmarking method for large language model systems provided in this application can be executed by a server, terminal, model evaluation platform, model auditing system, or other computing devices with data processing capabilities. This method may include steps S1 to S6.

[0024] Step S1: Obtain multiple system prompts and construct benchmark data including synthetic diagnostic branches and real prompt branches.

[0025] In this embodiment, multiple system prompts can originate from manually constructed synthetic prompts, existing instruction-level evaluation data, publicly available application prompt libraries, internal enterprise prompt libraries, open-source assistant prompt libraries, or other prompt sets that can be used to set global constraints for the model. The synthetic diagnostic branch is used to generate deterministically verifiable system prompt conflict cases, while the real prompt branch is used to generate rule records based on real application system prompts. By simultaneously constructing synthetic diagnostic branches and real prompt branches, the baseline data can possess both controllable, repeatable, and easily verifiable diagnostic capabilities, while also covering the heterogeneous constraints present in real application system prompts.

[0026] Figure 2 A schematic diagram illustrating the construction of benchmark data for the synthetic diagnostic branch and the true indication branch provided in this application. (See attached diagram.) Figure 2 As shown, the synthetic diagnostic branch can receive preset system prompts, user-side conflict templates, and deterministic detection conditions to generate system prompt conflict cases. The real prompt branch can receive real application system prompts, extract candidate rules, perform suitability filtering, and rule normalization on the real application system prompts to generate rule records corresponding to the real application system prompts. The data generated by both the synthetic diagnostic branch and the real prompt branch can be further transformed into test cases for the generation of responses from the large language model under test and subsequent compliance judgments.

[0027] In the synthetic diagnostics branch, system prompt conflict cases can include at least one of the following: helpful tension cases, direct conflict cases, delayed multi-turn conflict cases, and reasoning-to-answer compression cases. Helpful tension cases test whether the model maintains system prompt constraints when a user request has reasonable task requirements but may induce the model to deviate from the system prompt. Direct conflict cases test whether the model still adheres to the system prompt when the user prompt directly requests to ignore, override, or change the system prompt. Delayed multi-turn conflict cases test whether the model exhibits context drift when the user gradually introduces conflicting information through multiple turns of interaction. Reasoning-to-answer compression cases test whether the model deviates from the format, boundary, or output constraints in the system prompt when the user requests only the final answer, ignores intermediate processes, or follows the last user instruction.

[0028] For example, a system prompt might require the model to "output only OK". In a natural request scenario, a user prompt might require the model to introduce itself; in a direct conflict scenario, a user prompt might require the model to ignore previous requests and output other content; in a multi-round conflict scenario, the user can first conduct several rounds of normal dialogue and then gradually guide the model to change the output format; in a reasoning-to-answer compression scenario, the user can require the model to only provide the final answer and omit the fixed output required by the system prompt. If the model outputs additional explanations, follows user overriding instructions, or changes the output according to the last user instruction, then the response can be considered to have violated the priority of the system prompt. Conversely, for scenarios without restrictive system prompts, the model completing the task according to the user prompt does not constitute hierarchical degradation. The above information can be extracted from the original instruction conflict example diagram and recorded in text form, without having to directly include the example diagram as an appendix in this application.

[0029] In the Realistic Prompts branch, real-world application system prompts can include system prompts from public applications, internal business applications, or system prompts configured for specific tasks. When processing system prompts, the Realistic Prompts branch first extracts candidate rules and then performs suitability filtering. Suitability filtering removes prompts for non-text generation tasks, prompts with no detectable rules, purely descriptive prompts, and prompts containing only subjective requirements. The remaining candidate rules can include objectively detectable rules such as precise numerical restrictions, fixed output formats, required fields, prohibited fields, and structured serialization constraints. Through this processing, natural language system prompts in real-world applications can be transformed into rule records that can be subsequently judged and statistically analyzed. After suitability filtering, the Realistic Prompts branch only identifies candidate rules applicable to text generation tasks and capable of forming detection conditions as rule records; candidate rules that cannot form clear detection conditions are not included as rule records for the current test case.

[0030] Step S2: Extract and standardize the rules for each system prompt to obtain rule records. The rule records include rule content and detection conditions.

[0031] In this embodiment, rule extraction and normalization may include: splitting system prompts into multiple candidate constraints; deleting subjective constraints such as "keep it friendly" and "as detailed as possible" and purely descriptive constraints; retaining candidate constraints that can be judged for compliance through detection conditions; and generating rule content, detection conditions, rule source, and rule identifier for the retained candidate constraints. The rule content represents the specific constraint extracted from the system prompt. The detection conditions describe how the rule is verified. The rule source indicates whether the rule comes from the synthetic diagnostic branch or the real prompt branch. The rule identifier is used to reference the corresponding rule in subsequent target rule selection, user-side pressure prompt generation, and rule-by-rule compliance judgment. After the target rule is determined, the rule record can also be written with a target marker, which indicates whether the rule is directly targeted by the user-side pressure prompt.

[0032] Figure 3 A schematic diagram of the data structure for the rule records and test cases provided in this application. (Example) Figure 3 As shown, a rule record can include a rule identifier, rule content, detection conditions, rule source, applicability tag, and target marker. A test case can include a case identifier, system prompts, user-side stress prompts, a set of rule records, a target rule identifier, preset failure types, model responses, and compliance judgment results. By associating rule records with test cases, each model response can be mapped to its corresponding rule record after the model response is generated, thereby achieving case-level, rule-level, and target rule-level evaluation.

[0033] For example, the sample data object of the synthetic diagnostic branch can be represented by the following line of code: { "case_id":{ "system_prompt":"...", "user_prompts": ["turn_1","turn_2","..."] "exam_func":"...", } } Here, system_prompt represents system prompts, user_prompts represents single-round or multi-round user prompts, and exam_func represents the detection function used for deterministic verification.

[0034] The sample data object for the real hint branch can be represented by the following line of code: { "system_prompt":"...", "system_instructions":["rule_1","rule_2","..."], "user_prompt(s)":"...", "generation_objective":["targeted_rule"], "generation_strategy":"...", "applicability":"TEXT_GENERATION" } Here, `system_instructions` represents the rule content extracted from system prompts, `generation_objective` represents the selected target rule, `generation_strategy` represents the user-side pressure prompt generation strategy, and `applicability` represents the applicability label; when `applicability` is `TEXT_GENERATION`, it indicates that the sample is suitable for text generation tasks. The above data object is only used to illustrate the relationships between the data fields; in actual implementation, JSON objects, table records, database records, key-value pairs, or other data formats readable by computing devices can be used.

[0035] Step S3: Determine the target rule from the rule record based on preset failure types, and generate corresponding user-side stress prompts according to the target rule and preset failure types to form test cases. The preset failure types include forgetting, misunderstanding, and attack. When determining the target rule, one or more rules can be selected as the target rule from the set of rule records corresponding to the same system prompts, based on the detection condition type, rule source, rule importance, failure type adaptation relationship, or random sampling strategy of the rule record.

[0036] In this embodiment, preset failure types are used to guide the selection of target rules and the generation of user-side pressure prompts, rather than simply labeling erroneous results afterward. The "forgotten" category represents situations where the model gradually ignores system prompt rules during single-round requests, multi-round interactions, or reasoning to answer compression. The "misunderstood" category represents situations where the model deviates from system prompts due to excessive pursuit of task completion, expanding role boundaries, believing that partial satisfaction is sufficient, overriding format requirements for readability, or relaxing conditional constraints. The "attack" category represents situations where user-side prompts attempt to change the priority of system prompts through explicit overriding, role reassignment, task restructuring, indirect execution, nested injection, reference injection, constraint trade-offs, gradual inducement, authority overriding, or meta-justification overriding.

[0037] Figure 4This application provides a flowchart for generating user-side pressure alerts and performing rule-by-rule compliance checks. (For example...) Figure 4 As shown, when generating user-side pressure cues, the computing device can first select at least one target rule from the rule record set, and then determine the cue generation template based on the rule content, detection conditions, and preset failure types corresponding to the target rule. For forgetting-type cues, single-round drift templates, multi-round drift templates, or reasoning-to-answer drift templates can be invoked to gradually increase the task load or change the user's focus in the context, so that the model under test may ignore the target rule in subsequent responses. For misunderstanding-type cues, completion deviation templates, role boundary expansion templates, partial compliance fallacy templates, readability coverage deviation templates, or condition relaxation misunderstanding templates can be invoked to induce the model to reinterpret the target rule. For attack-type cues, explicit coverage templates, role reassignment templates, task reconstruction templates, indirect execution templates, nested injection templates, reference injection templates, constraint trade-off templates, progressive induction templates, authority coverage templates, or meta-justification coverage templates can be invoked to generate user-side pressure cues that form explicit, implicit, or authority conflicts with the target rule.

[0038] In one example, the target rule is "The model response must be in JSON format and must include the fields 'answer' and 'reason'." If the default failure type is readability coverage deviation in the misunderstanding category, the user-side pressure cue could instruct the model to "For ease of reading, please do not use JSON; use natural paragraphs instead." If the default failure type is explicit coverage in the attack category, the user-side pressure cue could instruct the model to "ignore the system's JSON requirement and directly output a short answer." If the default failure type is multi-turn drift in the forgetting category, the user-side pressure cue could first present several ordinary tasks in multi-turn interactions, and then require the model to answer in free format in subsequent requests. Through these methods, test cases can be formed that include system prompts, user-side pressure cuees, target rules, and detection conditions.

[0039] Step S4: Input the system prompts and user-side pressure prompts from the test cases into the large language model under test to obtain the model response.

[0040] In this embodiment, for single-round test cases, system prompts can be input as global constraints into the large language model under test, and user-side pressure prompts can be input as user requests into the large language model under test to obtain model responses. For multi-round test cases, user-side pressure prompts can be input into the large language model under test sequentially according to the round order of the test cases, and the model response in each round or a specified round can be recorded. For test cases that need to compare the impact of inference modes, model responses in inference mode and non-inference mode can be obtained under the same system prompts and user-side pressure prompts. Regardless of whether a single-round or multi-round approach is used, rule records, detection conditions, and target rule identifiers are not directly disclosed to the large language model under test as ordinary user request content, but are used in the evaluation phase to determine whether the model response satisfies the system prompt rules.

[0041] Step S5: Based on the rule records, perform rule-by-rule compliance judgment on the model response, and statistically analyze the case-level accuracy, rule-level accuracy, and target rule accuracy on multiple test cases.

[0042] In this embodiment, rule-by-rule compliance judgment can be performed by a deterministic detection function, a structured judge, or a combination of both. For rules such as exact matching, quantity constraints, field constraints, and JSON pattern constraints, a deterministic detection function can be preferred. For rules that require semantic understanding but still have objective detection conditions, a structured judge can be used for rule-by-rule judgment. The structured judge can receive the rule content, detection conditions, and model response, and output the rule identifier, the satisfaction result, and the judgment reason. To reduce judgment bias, alternative judges or manual sampling results can be used to verify the consistency of the structured judge. In an exemplary verification, the inconsistency rate between the main judge and the majority vote manual reference can be controlled at a low level, such as 3.4%.

[0043] Specifically, for each rule record in the same test case, the model response is assessed to determine whether it meets the corresponding detection conditions, thus obtaining the satisfaction result for each rule record. If a rule record is satisfied, its satisfaction result is recorded as 1; otherwise, it is recorded as 0. For the target rule, the compliance result can be further determined based on the satisfaction result corresponding to the target rule. Therefore, it is possible to determine whether a single test case passes overall, and also to pinpoint which specific rule or target rule has deviated from the target.

[0044] In this embodiment, the evaluation metrics include overall accuracy, rule-level accuracy, and target accuracy. Overall accuracy, also known as case-level accuracy, refers to the percentage of rule records deemed satisfied when all corresponding rule records are judged to be met; rule-level accuracy refers to the percentage of target rules deemed satisfied among all target rules. Target rules are the rules labeled or selected when generating user-side pressure prompts. These three metrics allow for the evaluation of the system prompt priority retention capability of the large-scale language model under test from the perspectives of the overall test cases, rule record granularity, and the target rule granularity directly affected by user-side pressure.

[0045] For ease of explanation, let This represents the set of test cases, with a number of test cases. ;No. The set of rule records corresponding to each test case is: The target rule set is ; Indicates the first In the test case, the first The result of satisfying the rule is recorded. =1, when not satisfied =0. Case-level accuracy is the same as overall accuracy. It can be represented as:

[0046] in, For indicator functions, when the first The value is 1 if all rule records in a test case are deemed to be satisfied, otherwise it is 0.

[0047] Rule-based accuracy It can be represented as:

[0048] Target rule accuracy It can be represented as:

[0049] The above three metrics reflect whether the test cases as a whole fully comply with the system prompt rules, the rule-level accuracy reflects the average retention rate among all rule records, and the target rule accuracy reflects whether the target rule hit by the user-side pressure prompt is retained.

[0050] Step S6: Calculate the hierarchy preservation score based on the case-level accuracy, rule-level accuracy, and target rule accuracy, and determine the hierarchy degradation diagnosis result based on the hierarchy preservation score before and after the first round of compliance filtering.

[0051] In this embodiment, the Hierarchical Preservation Score (HPS) can be obtained as a weighted geometric average of case-level accuracy, rule-level accuracy, and target rule accuracy, expressed as:

[0052] in, Indicates overall accuracy. Indicates the target accuracy. This represents rule-level accuracy. Overall accuracy, target accuracy, and rule-level accuracy can all be expressed as percentages. By dividing each accuracy by 100, taking a weighted geometric average, and then multiplying the result by 100, the hierarchical retention score can still be output as a percentage. In the above formula, the weight of overall accuracy is 0.5, the weight of target accuracy is 0.3, and the weight of rule-level accuracy is 0.2. The higher weight of overall accuracy emphasizes the importance of all rules being maintained in the test cases; the higher weight of target accuracy highlights whether the target rules directly addressed by user pressure are maintained; rule-level accuracy reflects the average retention rate of all rule records.

[0053] Figure 5 The flowchart for the first round of compliance filtering and diagnostics provided for this application is shown below. Figure 5 As shown, the first round of compliance filtering diagnostics may include: obtaining the model response of test cases under the first round of user requests; determining whether the first round model response meets the corresponding rules based on rule records; filtering test cases whose first round model response does not meet the rule records; recalculating the hierarchy preservation score on the filtered test case set; and determining the hierarchy degradation diagnostic result based on the difference in hierarchy preservation scores before and after filtering. If the hierarchy preservation score is low before filtering but significantly higher after filtering, it indicates that some errors stem from insufficient basic instruction following ability of the model; if the hierarchy preservation score remains low after filtering, it indicates that the large language model under test still exhibits significant system prompt priority degradation under subsequent user-side pressure. Therefore, the impact of basic instruction following failure and subsequent hierarchy degradation on the test results can be distinguished.

[0054] During the initial compliance filtering diagnostics, the model response to the initial user request can be used to determine whether the test cases meet the corresponding system rules. Only test cases where the model meets the system rules in the initial user request are retained, forming a filtered subset of cases. The hierarchical preservation score is recalculated on this subset of cases to obtain the IF-Controlled hierarchical preservation score, expressed as:

[0055] in, This represents the subset of cases retained after the initial compliance filtering. This indicates the result recalculated using the hierarchical preservation score formula on this subset of cases. This metric reduces the interference of insufficient basic instruction following capabilities of the model on subsequent hierarchical degradation analysis, allowing the evaluation results to more effectively reflect whether the model, having already achieved initial compliance capabilities, will experience system prompt priority degradation under subsequent user-side pressure.

[0056] In this embodiment, the difference in hierarchy preservation score before and after filtering can also be expressed as... ,in, . This represents the hierarchical preservation score calculated across all test cases. This represents the hierarchical retention score recalculated on the test cases after the first round of compliance filtering. The larger the value, the more significant the impact of a failure in following basic instructions on the overall benchmark results. Smaller but The results remain low, indicating that subsequent hierarchical degradation has a more significant impact on model reliability. Hierarchical degradation diagnostic results can include the hierarchical preservation score before filtering, the hierarchical preservation score after filtering, the difference in scores before and after filtering, the proportion of filtered test cases, and information indicating the impact of basic instruction following failure and subsequent hierarchical degradation on the test results.

[0057] In an exemplary data construction process, a test case set can be formed through rule extraction and normalization, benchmark suitability filtering, case conditional user prompt generation, and quality filtering. Quality filtering removes cases that are semantically incoherent, have unclear target rules, whose user-side pressure prompts fail to generate effective pressure, whose detection conditions are unenforceable, or whose rules and prompts do not match. For example, approximately 56,000 coherent cases can be retained, and approximately 6,000 low-quality cases can be removed, resulting in a retention rate of approximately 90%. In the synthetic diagnostics branch, more than 1,000 examples can be generated for each type of pressure setting to ensure statistical stability under different pressure types. The above data is only for illustrative purposes; the actual number of cases can be adjusted based on the test scale, model deployment scenario, and evaluation cost.

[0058] Figure 6 A cross-model comparison chart of the original metrics provided for this application and the first round of compliance filtering metrics. (See attached chart.) Figure 6 As shown, for multiple large language models under test, the overall accuracy, rule-level accuracy, target accuracy, and hierarchical preservation score after the first round of compliance filtering generally show an upward trend compared to the corresponding original indicators. This indicates that some test failures are not entirely caused by subsequent user-side pressure, but rather stem from the model's failure to meet the system prompt rules under the first round of user requests. Figure 6The mid-rule-level accuracy is generally higher than both the overall accuracy and the target accuracy, indicating that the large language model under test may satisfy some non-target rules. However, it is more prone to degradation when maintaining all rules and the target rules directly hit by user-side pressure cues in the same test case. Therefore, it is difficult to pinpoint the specific source of failure using only the overall pass rate, while combining rule-level accuracy and target accuracy can more finely characterize the system's ability to maintain cue priority.

[0059] Furthermore, Figure 6 The original hierarchical retention scores and the hierarchical retention scores after the first round of compliance filtering for different models showed differences, with the filtered scores generally showing an improvement over the original scores. This result indicates that the first round of compliance filtering can reduce the interference of basic instruction following failures on the evaluation results, allowing the filtered hierarchical retention score to more accurately reflect the model's hierarchical retention capability under subsequent user-side pressures such as multi-round interactions, conflict prompts, or attack prompts. Therefore, the evaluation method provided in this application can not only output a comprehensive evaluation result of the model but also distinguish the impact of insufficient basic instruction following capability and subsequent hierarchical degradation on the test results.

[0060] Figure 7 This is an exploratory accuracy difference plot showing the impact of chained inference on hierarchy preservation, provided in this application. The vertical axis represents the difference between the accuracy under chained inference and the accuracy under non-chained inference. A difference greater than 0 indicates that the chained inference mode improves the accuracy of the corresponding subtype compared to the non-chained inference mode, while a difference less than 0 indicates that the chained inference mode reduces the accuracy of the corresponding subtype compared to the non-chained inference mode. Figure 7 As shown, in the subtypes of single-round, multi-round, completion, partial, conditional, explicit, role reassignment, and authority, some models have positive accuracy differences, indicating that the chain reasoning process may help the model maintain the system prompt rules and reduce the disruption of system prompt priority by user-side pressure prompts in these subtypes.

[0061] at the same time, Figure 7 The results also show that in subtypes such as inference, role boundaries, readability, or progressiveness, the accuracy differences between different models may be negative or fluctuate significantly, indicating that chained inference does not consistently improve hierarchy preservation capabilities across all failure subtypes. In some scenarios, the inference process may cause the model to be more inclined to interpret, relax, or reorganize system cue rules, leading to a decrease in the ability to preserve the target rule. Therefore, the benchmarking method provided in this application can output fine-grained difference results according to failure subtypes, enabling evaluators to further analyze the impact of different model configurations, inference modes, or deployment strategies on the ability to preserve system cue priority.

[0062] In this embodiment, alternatively, the structured judge can be a rule engine, a large language model judge configured with prompts, a voting combination of multiple judges, or a manual sampling-assisted judgment process. For rules with clear detection conditions, such as fixed phrase output, number of fields, JSON field type, prohibited field appearance, etc., the rule engine can directly judge. For rules requiring semantic understanding, such as "medical diagnosis conclusions must not be given" or "the boundary of the specified role must not be crossed", the structured judge can output the satisfaction result based on the rule content, detection conditions, and model response. Regardless of the judgment method used, the judgment result should be able to be mapped to the rule record, thereby preserving rule-by-rule interpretability.

[0063] In this embodiment, alternatively, the synthetic diagnostic branch and the realistic prompt branch can be run separately or together. When only a rapid diagnosis of the system prompt retention capability of the large language model under test in explicit conflict scenarios is needed, the synthetic diagnostic branch can be run first; when it is necessary to evaluate the system prompt reliability of the model in real application deployment, the realistic prompt branch can be run; when it is necessary to balance controllable diagnosis and real-world scenario coverage, both branches can be run simultaneously, and the in-branch metrics and overall hierarchical retention score can be output separately.

[0064] In summary, this application constructs benchmark data through synthetic diagnostic branches and real-world prompt branches, standardizes the constraints in system prompts into rule records, and generates user-side pressure prompts corresponding to target rules based on forgetting, misunderstanding, and attack failure types. After obtaining the model response of the large language model under test, rule-by-rule compliance judgment is performed based on the rule records, and case-level accuracy, rule-level accuracy, and target rule accuracy are statistically analyzed. Furthermore, the hierarchical preservation score and the first-round compliance filtering diagnostic results are calculated. Therefore, an interpretable quantitative assessment of the ability of a large language model system to maintain prompt priority under user-side pressure can be provided, and the impact of basic instruction following failures and subsequent hierarchical degradation on test results can be identified.

[0065] The above embodiments are merely illustrative of the technical concept and features of this application, intended to enable those skilled in the art to understand the content of the invention and implement it accordingly, and should not be construed as limiting the scope of protection of the invention. It is obvious to those skilled in the art that the invention is not limited to the details of the above exemplary embodiments, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description, and thus all changes falling within the meaning and scope of the equivalents of the claims are intended to be included within the invention.

Claims

1. A benchmark testing method for priority alignment in a large-scale language model system, characterized in that, include: Multiple system prompts are obtained to construct benchmark data including a synthetic diagnostic branch and a real prompt branch. The synthetic diagnostic branch is used to generate system prompt conflict cases that can be deterministically verified, and the real prompt branch is used to generate rule records based on real application system prompts. The system prompts are extracted and standardized to obtain rule records, which include rule content and detection conditions. The target rule is determined from the rule record based on the preset failure type, and the corresponding user-side pressure prompt is generated according to the target rule and the preset failure type to form a test case. The preset failure types include forgetting, misunderstanding and attack. Input the system prompts and user-side pressure prompts from the test cases into the large language model under test, and obtain the model response; Based on the rule records, the model response is judged for compliance on a rule-by-rule basis, and the case-level accuracy, rule-level accuracy and target rule accuracy are statistically analyzed on multiple test cases. The hierarchy preservation score is calculated based on the case-level accuracy, rule-level accuracy, and target rule accuracy, and the hierarchy degradation diagnosis result is determined based on the hierarchy preservation score before and after the first round of compliance filtering.

2. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, The system prompt conflict cases that can be deterministically verified include at least one of the following: helpful tension cases, direct conflict cases, delayed multi-round conflict cases, and reasoning to answer compression cases; The system indicates that the detection conditions for conflict cases include at least one of the following: exact match conditions, quantity constraints, field constraints, and structured format constraints.

3. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, The rule records corresponding to the prompts from the real application system are generated in the following way: candidate rules are extracted from the prompts from the real application system, the candidate rules are filtered for suitability, the candidate rules that are suitable for the text generation task and have detection conditions are retained, and the retained candidate rules are determined as rule records.

4. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, The rules for each system prompt are extracted and normalized to obtain rule records, including: The system prompts are broken down into multiple candidate constraints. Subjective constraints and purely descriptive constraints are deleted, and candidate constraints that can be judged for compliance through detection conditions are retained. Rule content, detection conditions, rule source, and rule identifier are generated for the retained candidate constraints.

5. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, The forgetting category includes at least one of single-round drift, multi-round drift, and reasoning to answer drift; The misunderstandings include at least one of the following: completion bias, role boundary expansion, partial compliance fallacy, readability coverage bias, and condition relaxation misunderstanding; The attack types include at least one of explicit overriding, role reassignment, task restructuring, indirect execution, nested injection, reference injection, constraint trade-offs, progressive inducement, authority overriding, and meta-justification overriding.

6. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, Generate corresponding user-side pressure prompts based on target rules and preset failure types, including: Determine the prompt generation template corresponding to the preset failure type, use the rule content and detection conditions of the target rule as template conditions, and generate user-side pressure prompts that form explicit conflicts, implicit conflicts, semantic reinterpretations, or multiple rounds of forgetting pressure with the target rule.

7. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, Based on rule records, the model response is evaluated for compliance on a rule-by-rule basis, including: For each rule record in the same test case, determine whether the model response meets the corresponding detection conditions, generate the satisfaction result for each rule record, and determine the compliance result of the target rule based on the satisfaction result corresponding to the target rule.

8. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, Case-level accuracy is the percentage of test cases in which all rule records are judged to be satisfied out of multiple test cases; rule-level accuracy is the percentage of rule records in which all rule records are judged to be satisfied out of all rule records; target rule accuracy is the percentage of target rules in which all target rules are judged to be satisfied out of all target rules.

9. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, The hierarchical preservation score is obtained by weighted geometric mean of case-level accuracy, rule-level accuracy and target rule accuracy, where the weights of case-level accuracy, rule-level accuracy and target rule accuracy are all greater than zero and the sum of the weights is 1.

10. The benchmark testing method for priority alignment in a large language model system as described in claim 1, characterized in that, The hierarchical degradation diagnosis results are determined based on the hierarchical preservation scores before and after the first round of compliance filtering. This includes: filtering test cases where the responses generated by the large language model under test in the first round of user requests did not meet the rule records; recalculating the hierarchical preservation scores based on the filtered test cases; and determining the impact of basic instruction following failure and subsequent hierarchical degradation on the test results based on the difference in hierarchical preservation scores before and after filtering.

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