Auto-Evaluator Opportunities

Lance Martin's blog post discusses the release of an open-source auto-evaluator tool designed to improve the quality of question-answering (QA) systems using large language models (LLMs). The tool, available as a free hosted app and API, evaluates the quality of QA chains by auto-generating and grading test sets for given input documents. It addresses common issues such as hallucination and poor answer quality by allowing users to experiment with different QA chain configurations and components. Inspired by recent work from Anthropic and OpenAI, the auto-evaluator combines model-written and model-graded evaluations in a single workspace, facilitating modular testing with LangChain's abstraction. The app supports various retriever approaches, such as k-Nearest Neighbor, SVMs, and TF-IDF, and highlights areas for improvement, including file handling, prompt refinement, and model selection. The post encourages contributions to the open-source project, particularly in enhancing file transfer efficiency, refining prompts for model-graded evaluations, and exploring additional retriever options.

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