LLM Chatbot Quality Benchmarking for Pre/Post-Processing Changes
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Solution Overview
Problem
Existing methods fail to effectively assess how changes in pre- and post-processing tasks of LLM-based chatbots impact the overall quality of chatbot performance, particularly in understanding user queries and generating accurate responses.
Innovation Solution
A dynamic quality benchmark system is implemented to evaluate LLM-based chatbots, using a database with human feedback for scoring and comparing perplexity scores of chatbot responses before and after changes, enabling automated testing and ensuring quality is maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated testing is implemented to assess chatbot quality after changes, then assessment efficiency is improved, but measurement precision of quality impact is worsened
Solution Approach 1:
The patent introduces perplexity scores as an intermediary metric to bridge automated testing and quality assessment. Instead of directly measuring quality impact through complex human evaluation, the system uses perplexity scores computed from model predictions on test questions as a mediator. This allows automated testing to efficiently assess quality changes while maintaining measurement precision through the statistical properties of perplexity scores.
Solution Approach 2:
The patent replaces manual quality assessment mechanisms with automated computational methods. Specifically, it substitutes human evaluation with automated perplexity score computation and statistical testing (t-tests, confidence intervals). This substitution maintains measurement precision by using rigorous statistical methods while dramatically improving assessment efficiency through automation.
2Measurement precision
If comprehensive quality assessment is performed on all chatbot changes, then measurement precision is improved, but loss of time is worsened
Solution Approach 1:
The patent applies partial action by focusing assessment only on the most relevant aspects of chatbot quality. Instead of comprehensively evaluating all possible quality dimensions, it selectively measures perplexity scores on representative test questions. This partial assessment approach maintains sufficient measurement precision for detecting meaningful quality changes while significantly reducing the time required compared to comprehensive evaluation.
Solution Approach 2:
The patent performs preliminary action by pre-computing perplexity scores for both the old and new chatbot versions before comparison. Test questions are prepared in advance, and baseline perplexity scores are computed beforehand. This preliminary preparation enables rapid comparison and assessment when changes are made, reducing the actual assessment time while maintaining measurement precision.
3Measurement precision
If manual evaluation methods are used to assess chatbot quality, then measurement precision is improved, but productivity is worsened
Solution Approach 1:
The patent replaces manual evaluation mechanisms with automated computational systems. It substitutes human evaluators with algorithmic perplexity score computation and statistical testing. This substitution maintains measurement precision by using rigorous statistical methods (t-tests, confidence intervals) while dramatically improving productivity through automation and parallel processing of test questions.
Solution Approach 2:
The patent uses copying by creating synthetic test questions that replicate real user queries and computing perplexity scores that copy the statistical properties of human evaluation. Instead of requiring actual human evaluators for every assessment, the system copies evaluation criteria into computational metrics, maintaining precision while enabling high-speed automated assessment.
Data Source
AI summary
One example method for evaluating a quality of chatbot answers after changes have been made to internal chatbot pre-processing tasks and/or post-processing tasks, includes filtering, based on information identifying changes that have been made to a reference version of a chatbot, a set of representative questions that have been posed by one or more users to the reference version of the chatbot, to obtain a test set of test questions related to the changes, obtaining test answers, generated by a new version of the chatbot, to the test questions, scoring the test answers, and performing an automated testing process that includes comparing scores of the test answers with scores of answers provided by the reference version of the chatbot to the test questions.