LLM Response Evaluation via Action Path Extraction
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Solution Overview
Problem
Current mechanisms for evaluating responses from language models are inefficient and unable to accurately assess the accuracy and completeness of responses to product help inquiries, particularly in specific domains, leading to the provision of irrelevant, inaccurate, or harmful content.
Innovation Solution
A system that extracts action paths from language model responses, generates contextual embeddings, measures semantic similarity and path coverage, and calculates a custom evaluation metric to assess the accuracy and completeness of responses, allowing for real-time evaluation and iterative refinement of the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLM is finetuned to specific domain using traditional methods, then domain-specific accuracy is improved, but training cost and time increase significantly
Solution Approach 1:
The evaluation process is segmented into multiple independent components: prompt generation, action path extraction, contextual embedding generation, and metric calculation. This allows parallel processing and avoids the need for time-consuming full-model finetuning while maintaining domain-specific accuracy through targeted evaluation.
Solution Approach 2:
The patent introduces an intermediary evaluation system that acts as a mediator between the LLM and domain-specific requirements. Instead of directly finetuning the LLM, the system uses prompt engineering and action path analysis to bridge the gap, reducing training time while preserving accuracy.
2Measurement precision
If LLM is finetuned to specific domain, then response accuracy is improved, but computational resources and cost increase
Solution Approach 1:
The patent extracts only the essential action paths from domain-specific documentation and uses them for evaluation, rather than processing entire datasets. This extraction approach reduces computational resources while maintaining response accuracy by focusing on critical domain-specific elements.
Solution Approach 2:
The system changes evaluation parameters by using contextual embeddings and action path metrics instead of traditional full-model evaluation. This allows accurate domain-specific assessment with reduced computational overhead by evaluating specific response characteristics rather than entire models.
3Productivity
If traditional evaluation methods are used, then model performance is assessed, but evaluation accuracy and completeness are insufficient
Solution Approach 1:
The patent adds new evaluation dimensions by introducing action path extraction and contextual embedding analysis. Instead of relying on traditional scalar metrics, the system evaluates responses across multiple dimensions including semantic similarity, path coverage, and action completeness, thereby improving evaluation accuracy while maintaining efficiency.
Solution Approach 2:
The patent replaces traditional mechanical evaluation methods with semantic-based assessment using contextual embeddings. This substitution allows for more accurate and complete evaluation of domain-specific responses by capturing semantic meaning rather than relying on rigid keyword matching.
4Reliability
If comprehensive model evaluation is performed, then response quality is assessed, but computing resources are excessively consumed
Solution Approach 1:
The patent applies partial evaluation by focusing on action paths and key semantic elements rather than evaluating every aspect of the model response. This partial approach maintains reliable quality assessment while significantly reducing computing resource consumption by evaluating only the most critical response components.
Data Source
AI summary
A system and method for evaluating performance of a model used in providing a response to a product help inquiry includes receiving the product help inquiry, classifying the product help inquiry as being associated with a topic related to a product, and retrieving a path of actions provided in a help documentation associated with the topic. A prompt is also generated based on the product help inquiry for transmission to the model and a response is provided by the model, before a path of actions included the response is extracted. Contextual embeddings for the extracted path are generated and semantic similarities between contextual embeddings for the extracted path and embeddings generated for an expected response are measured. By generating contextual embeddings for the extracted path instead of the entire response, resources required for evaluating the response are significantly reduced. A path coverage metric is measured for the extracted path. A total evaluation value for the response is determined based on a weighted combination of one or more of the measured semantic similarity, path coverage metric, a path length metric or a path frequency metric.