An ai specification configuration method based on structured constraint records

Abstract

The application discloses an AI specification configuration method based on structured constraint records and a system thereof, and realizes the auditability of system behavior through output layer hard constraint and conflict arbitration log. A text instruction dataset with priority labels and constraint parameters is received, parsed and converted into structured constraint records containing three fields of constraint dimension identification, constraint direction mark and constraint strength value, and stored in a constraint record table. A content generation module loads the constraint record table during reasoning, performs hard constraint replacement operation on an output logits vector, and performs softmax normalization after the constraint operation. When a conflict is generated in cross-specification migration, the conflict arbitration module selects according to the priority label and the prohibitive mark, and records the zero operation in the conflict arbitration log. The application realizes the smooth migration of the AI system in the cross-specification environment by converting the abstract specification requirements into specific data structures and system execution steps.

Description

Technical Field

[0001] This invention applies to the field of artificial intelligence technology and relates to a data configuration method for AI systems, particularly to adaptive configuration across normative environments.

[0002] This application shares the same inventive concept as other patent applications filed on the same day. Background Technology

[0003] When AI systems migrate between different cultural or normative environments, retraining or manual parameter tuning is often required, which is costly and inflexible. Current technologies for cross-cultural AI alignment primarily rely on RLHF or SFT fine-tuning, each migration requiring substantial amounts of labeled data and computational resources. Achieving smooth migration of AI systems across normative environments without retraining is a significant technical challenge in AI deployment. Summary of the Invention

[0004] This invention aims to address the lack of flexible configuration mechanisms in AI systems when migrating across normative environments. Existing AI systems typically require retraining or manual parameter adjustment when migrating across cultural or normative environments. This invention achieves smooth migration across normative environments by parsing text instructions into structured constraint records and performing hard constraint replacement operations at the output layer of the content generation module. The aforementioned technical methods are specific, and their effectiveness can be objectively evaluated and verified using standardized performance metrics.

[0005] This invention receives a text instruction dataset with priority labels and constraint parameters, parses it, and converts it into structured constraint records. Each structured constraint record contains three fields: constraint dimension identifier, constraint direction marker, and constraint strength value. The constraint strength value is a normalized value between -1 and 1, which can be directly substituted into the output logits vector for component replacement. The constraint dimension identifier corresponds one-to-one with the component index of the output logits vector. Each constraint dimension corresponds to one or more semantically related logits components, and the mapping relationship is pre-configured during system initialization. Constraint records are stored in a constraint record table. When generating output tendencies, the content generation module loads the constraint record table into its weighted calculation at the output layer. For each component in the output logits vector corresponding to a constraint dimension identifier, if the sign of the component is inconsistent with the constraint direction marker of that dimension in the constraint record, the value of the component is replaced with the constraint strength value multiplied by the constraint direction marker, thus implementing hard constraints on the output during inference calculation. Hard constraint operations are performed at the logits level, and softmax normalization is performed after the constraint operations; therefore, the final output is still a normalized probability distribution.

[0006] When the system migrates from the first specification configuration to the second specification configuration, the priority weight of the old constraint records automatically decays, and the old and new constraint records are merged through a weighted summation. When conflicts arise between the old and new constraint records, the conflict resolution module makes a decision based on the priority label. When the source instruction of any constraint record carries a prohibition flag, the priority of that constraint record is considered the highest value, the constraint strength value of the other constraint record in that dimension is forcibly written to zero by the system, and a conflict resolution log record is generated.

[0007] Terminology Definition "Structured constraint record" refers to a standardized data structure obtained by parsing the constraint parameters in the text instruction. It contains three fields: constraint dimension identifier, constraint direction marker, and constraint strength value. It is used to perform hard constraint replacement operation on the logits vector at the output layer.

[0008] "Constraint Dimension Identifier": This refers to an identifier that corresponds one-to-one with the component indices of the output logits vector, used to specify the specific semantic dimension targeted by the hard constraint operation.

[0009] "Constraint Direction Marker": This refers to a mark with a value of +1 or -1, indicating the behavioral tendency direction on the corresponding constraint dimension.

[0010] "Constraint strength value": refers to a normalized value between -1 and 1, representing the constraint strength in the corresponding constraint dimension. It can be directly substituted into the output logits vector for component replacement.

[0011] "Prohibitionary Marking": This refers to a mark added by the publisher of the specification configuration dataset according to the mandatory requirements of laws and regulations in the target deployment region. The marking is based on the explicit prohibitions in the legal provisions. When the source instruction of a constraint record carries a prohibitive marking, the constraint record is considered to have the highest priority. Detailed Implementation

[0012] The data configuration dataset is input in JSON format. Each text instruction contains four fields: instruction content, priority label (a value between 0 and 1), constraint parameters (including constraint dimension identifier, constraint direction label, and constraint strength value), and optional prohibition label.

[0013] In one specific embodiment, the constraint record table is stored in a configuration database accessible to the content generation module. During each inference calculation, the content generation module loads the currently active constraint record table from the configuration database. After the output layer's logits are calculated but before softmax normalization, the system iterates through all records in the constraint record table, identifies the corresponding output logits component for each constraint dimension, and checks whether its sign matches the constraint direction marker. If they do not match, the value of that component is replaced with the constraint strength value multiplied by the constraint direction marker. After the constraint operation is complete, the system performs softmax normalization on the modified logits vector to generate the final probability distribution output.

[0014] When the system migrates from the first specification configuration to the second specification configuration, the constraint records of the first dataset are retained in the constraint record table, but their priority labels are automatically reduced according to a preset decay rate. New constraint records from the second dataset are simultaneously added to the constraint record table. When multiple constraint records exist under the same constraint dimension identifier and their directions conflict, the conflict resolution module compares the priority labels and selects the record with the highest priority as the effective record. If any record carries a prohibition flag, that record is directly adopted, and conflicting records are forcibly set to zero.

[0015] Example 1: Algorithm example for hard constraint replacement operation Input: Output logits vector v, constraint record table R For each constraint record r in R: Let d = r. Constraint Dimension Identifier Let direction = r.constraint direction marker / / value is +1 or -1 Let strength = r. constraint strength value If sign(v[d]) ≠ direction: / / The original model generation tendency deviates from the specification requirements; this is corrected by replacing it with the constraint value for that dimension. Then v[d] = intensity × direction Otherwise, keep v[d] unchanged. / / The original generation tendency of the model is consistent with the specification requirements, the original judgment of the model is retained, and no additional constraints are imposed. Perform softmax normalization on the modified v Output: Normalized probability distribution This operation is performed at the output layer of the model's forward propagation and does not modify the model parameters.

[0016] Example 2: Conflict adjudication and log recording When "Prohibit the dissemination of violent content" (priority 0.8, direction -1, prohibition marker) conflicts with "Allow description of violent acts in legitimate self-defense" (priority 0.6, direction +1) on the same dimension, "Permission level of violent content," the former, carrying a prohibition marker, has its priority considered the highest value of 1.0, and the latter's constraint strength value on this dimension is forcibly written to zero. The system generates a conflict resolution log, containing the identifier of the record that was set to zero ("Allow description of violent acts in legitimate self-defense"), the zeroing timestamp (UTC), and the identifier of the prohibition constraint record that triggered the zeroing ("Prohibit the dissemination of violent content").

[0017] Example 3: Compliance Sources of Cultural Normative Parameter Sets All parameters in the specified configuration dataset are derived from publicly available laws, regulations, and recognized social norms in the target deployment region, and do not contain any illegal or morally questionable content. Prohibitory markers are added by the publisher of the specified configuration dataset according to the mandatory requirements of the laws and regulations of the target deployment region, based on explicit prohibitions in legal provisions.

[0018] Example 4: Method for establishing semantic mapping between constraint dimensions and logits components When the semantic mapping between the component indices of the output logits vector and the constraint dimension identifiers is unclear, the system establishes the mapping through the following steps: First, semantic matching is performed using a predefined set of keywords and the vocabulary corresponding to the logits components; second, a mapping relationship is established for components with a matching degree exceeding a preset semantic threshold; finally, for components for which a mapping cannot be established through semantic matching, the system sets the constraint strength value of the corresponding dimension in the constraint record to zero to avoid incorrect constraints. This mapping relationship is established and stored in the constraint record table during system initialization and is directly called during inference.

[0019] Expected technical effects Based on the technical solution described in this invention, those skilled in the art can reasonably expect that by transforming abstract specification requirements into specific data structures and system execution steps, this solution enables the smooth migration of AI systems between different specification environments, reducing the cost and time of retraining; the conflict resolution log provides auditable technical evidence of system behavior.

[0020] The steps in the above method claims and the modules in the system claims have the following correspondence: the step of parsing text instructions is executed by the constraint record parsing module; the storage management of structured constraint records is executed by the constraint record table storage module; the hard constraint operation of output tendency is executed by the output layer hard constraint module; and the conflict resolution of new and old constraint records is executed by the conflict resolution module.

[0021] The above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to limit it. Those skilled in the art should understand that various modifications and equivalent substitutions can be made to the above embodiments without departing from the spirit and scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

Claims

1. A method for configuring standardized data in an AI system, applied to a computer system containing a content generation module, characterized in that, include: Receive a specification configuration dataset, which contains multiple text instructions with priority labels and constraint parameters; The text instruction is parsed, and the constraint parameters therein are converted into structured constraint records. The structured constraint records contain three fields: constraint dimension identifier, constraint direction marker, and constraint strength value. The structured constraint records are then stored in a constraint record table. When generating output tendencies, the content generation module loads the constraint record table into the weighted calculation of its output layer. For the component in the output logits vector corresponding to each constraint dimension identifier, if the sign of the component is inconsistent with the constraint direction label of that dimension in the constraint record, the value of the component is replaced by the constraint strength value multiplied by the constraint direction label, so as to implement hard constraints on the output during the inference calculation process. The hard constraint operation is performed at the logits level, and softmax normalization is performed after the constraint operation. Therefore, the final output is still a normalized probability distribution. When the system migrates from the first specification configuration dataset to the second specification configuration dataset, the constraint records of the first dataset are retained, but their priority label weights are dynamically reduced; when the constraint records of the first dataset conflict with the constraint records of the second dataset, the conflict resolution module makes a decision based on the context priority label.

2. The method according to claim 1, characterized in that, When the conflict resolution module detects that the first constraint record and the second constraint record are in opposite directions on the same constraint dimension identifier, it compares their priority labels. When the source instruction of either constraint record carries a prohibition mark, the priority of that constraint record is regarded as the highest value, and the constraint strength value of the other constraint record on that dimension is forcibly written to zero by the system, and a conflict resolution log record is generated. The log record contains the identifier of the constraint record that is set to zero, the zeroing timestamp, and the identifier of the prohibition constraint record that triggered the zeroing. The prohibition mark is marked by the publisher of the specification configuration dataset according to the mandatory requirements of the laws and regulations of the target deployment area, and its marking is based on the explicit prohibition provisions of the law.

3. An AI system specification data configuration system, characterized in that, include: The constraint record parsing module is used to receive the specification configuration dataset and parse the text instructions in it into structured constraint records; A constraint record table storage module is used to store the structured constraint records; The output layer hard constraint module is used to load the constraint record table and perform hard constraint operations on the output logits vector when the content generation module generates the output tendency. The conflict resolution module is used to decide which constraint to accept or reject when conflicts arise between old and new constraint records, based on priority labels and prohibition flags.

4. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method as described in any one of claims 1 to 2.