LANGCHAIN — Auto Evaluator
In the software world, the moment you start using someone else’s software, you are living in their world, under their philosophy. — Richard Stallman
LangChain has recently introduced an open-source auto-evaluator tool for grading LLM question-answer chains and is now offering a hosted app and API for expanded usability. This article outlines the functionalities, usage, and opportunities for improvement of the auto-evaluator tool.
The auto-evaluator aims to address the limitations in evaluating the quality of QA systems and using this evaluation to guide improved QA chain settings and components. It combines recent advancements in model-written evaluations and model-graded evaluation, making it easy to configure QA with modular components for testing.
Usage
The auto-evaluator can be used in two ways:
- Demo: Pre-loaded with a document and a set of question-answer pairs, users can configure QA chains and run experiments to evaluate the relative performance.
- Playground: Users can input a document to evaluate various QA chains on, optionally including a test set of question-answer pairs related to the document.
Opportunities for Improvement
- File Handling:
- File transfer from client to back-end is slow, and there is an opportunity to optimize this process by stripping images prior to transfer.
- Model-Written-Evaluations:
- There is an opportunity to improve the generation of QA pairs by considering the overall context of the input.
- Retrievers:
- The auto-evaluator makes it easy to add and test various retrievers, and there is room for improvement in the test set composition.
- Model-Graded Eval:
- There is variability in answer scoring across prompts, and future work should focus on refining the prompts for model-graded evaluation.
Conclusion
Contributions related to file handling, prompts, models, or retrievers are a few of the highest impact areas where the open-source auto-evaluator tool can be enhanced.
This article has provided an overview of the LangChain auto-evaluator tool, its usage, and opportunities for improvement. It aims to guide developers in understanding the functionalities of the tool and encourage contributions to enhance its capabilities.