LLM Output Scoring Model for Semantic Evaluation Accuracy
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Solution Overview
Problem
Conventional evaluation tools struggle to accurately assess the output of language models, particularly large language models (LLMs) with Retrieval Augmented Generation (RAG), due to difficulties in detecting inaccuracies when outputs are not worded similarly to expected outputs, leading to potential mislabeling of accurate responses as inaccurate.
Innovation Solution
A scoring model that receives input from a language model and evaluates its output based on various factors, including scoring guidelines, expected outputs, and user queries, to provide an accurate score and rationale for the output's quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional evaluation tools compare expected output and actual output directly, then the evaluation process is simple, but the measurement precision deteriorates because outputs with different wording but same meaning are falsely labeled as inaccurate
Solution Approach 1:
The patent introduces a scoring model as an intermediary between the expected output and actual output comparison. This scoring model evaluates multiple factors including semantic equivalence, information completeness, and formatting requirements, rather than performing direct string comparison. The scoring model acts as a mediator that translates the evaluation task into a nuanced assessment, resolving the contradiction by maintaining high measurement precision while managing complexity through modular scoring criteria.
2Measurement precision
If the scoring model evaluates multiple factors including semantic equivalence and information completeness, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The scoring model is segmented into distinct evaluation criteria: semantic equivalence assessment, information completeness checking, formatting validation, and other factor evaluations. Each criterion operates independently with its own rules and weightings. This segmentation allows the system to maintain high measurement precision through comprehensive evaluation while managing complexity by organizing the scoring logic into discrete, manageable components that can be implemented and maintained separately.
3Reliability
If conventional tools use simple string comparison, then the ease of operation is high, but the reliability deteriorates because accurate responses with different wording are mislabeled as inaccurate
Solution Approach 1:
The patent changes the evaluation parameters from simple string matching to multi-dimensional assessment including semantic equivalence, information completeness, and formatting compliance. By altering the parameters of evaluation from binary match/no-match to weighted scoring across multiple dimensions, the system achieves higher reliability in identifying accurate responses while managing operational complexity through structured scoring guidelines and automated calculation processes.
Data Source
AI summary
Systems and methods for scoring language model outputs using a scoring model are provided. At least one input information and at least one output from a language model may be received as input by a scoring language model. The scoring language model may be configured to score the at least one output based on the at least one input information to yield an output score. A user interface may output the output score and the output from the language model.