LLM Response Evaluation Using a Domain-Aware Judge Model
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Solution Overview
Problem
Existing automated evaluation systems for large language models (LLMs) struggle with accurately interpreting user-supplied questions that deviate from expected inputs, leading to unpredictable inaccuracies, particularly in specialized domains like tax law, due to the lack of a robust procedure for interpreting variations in question wording or phrasing.
Innovation Solution
A second LLM is used as a judge to evaluate responses from a first LLM, infused with domain knowledge by leveraging observations from initial tax expert evaluations, and determining prompt context to improve evaluation accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated evaluation is implemented without domain knowledge infusion, then evaluation speed and scalability improve, but evaluation accuracy deteriorates due to inability to interpret question variations
Solution Approach 1:
The system performs preliminary actions by infusing domain knowledge into the judge LLM before the actual evaluation process. This includes pre-loading tax law knowledge, evaluation criteria, and interpretation rules into the judge model's context, enabling it to accurately evaluate responses even when questions deviate from expected formats.
Solution Approach 2:
The judge LLM acts as an intermediary between the automated evaluation system and the domain-specific evaluation requirements. It translates and interprets varied user questions into standardized evaluation criteria, bridging the gap between automated processing and domain expertise without requiring manual expert intervention for each evaluation.
2Measurement precision
If expert evaluation is used for specialized domains, then evaluation accuracy improves, but evaluation cost and time consumption increase
Solution Approach 1:
The system creates a virtual copy of expert knowledge by training and infusing domain expertise into the judge LLM. This digital replica of expert knowledge enables automated evaluations that mirror expert accuracy without requiring actual expert time investment, allowing parallel processing of multiple evaluations simultaneously.
Solution Approach 2:
The judge LLM performs self-service by autonomously evaluating responses using embedded domain knowledge and evaluation criteria. It independently interprets questions, assesses responses against tax law requirements, and generates evaluations without requiring external expert intervention, thereby eliminating time losses associated with human expert scheduling and review.
3Device complexity
If automated evaluation interprets question variations without domain knowledge, then system simplicity is maintained, but reliability deteriorates due to unpredictable inaccuracies
Solution Approach 1:
The judge LLM serves multiple functions within a single system component: it interprets varied user questions, applies domain-specific evaluation criteria, assesses response quality, and generates evaluations. This multi-functionality maintains system simplicity by consolidating what could be separate complex modules into a single versatile AI agent with embedded domain knowledge.
Data Source
AI summary
At least one processor may receive a user query and generate a first prompt including at least the user query. The at least one processor may input the first prompt to a first large language model (LLM) and receive a first response from the first LLM. The at least one processor may generate a second prompt including a context of a processing state of a computing system and/or an expected response, input the second prompt to a second LLM different from the first LLM, and receive a second response from the second LLM. The at least one processor may determine a validity verdict of the first response using the second response. The at least one processor may generate an answer to the user query, wherein the answer includes the first response for a valid verdict or omits the first response for an invalid verdict.