Integrating Spade: Synthesizing Assertions for LLMs into My OSS Project

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  • uptrain

    UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

  • d. Using an integer programming optimizer to find the optimal evaluation set with maximum coverage and respect failure, accuracy, and subsumption constraints

    Their results are impressive. You can look at the SPADE paper for more details: https://arxiv.org/pdf/2401.03038.pdf

    2. Running these evaluations reliably is tricky: Recently, using LLMs as evaluators has emerged as a promising alternative to human evaluations and has proven quite effective in improving the accuracy of LLM applications. However, difficulties still exist when running these evals reliably, i.e. high correlation with human judgments and stability across multiple runs. UpTrain is an open-source framework for evaluating LLM applications that provide high-quality scores. It allows one to define custom evaluations via GuidelineAdherence check, where one can determine any custom guideline in plain English and check if the LLM follows it. Additionally, it provides an easy interface to run these evaluations on production responses with a single API call. This allows one to systematically leverage frameworks like UpTrain to check for wrong LLM outputs.

    I am one of the maintainers of UpTrain, and we recently integrated the SPADE framework into our open-source repo (https://github.com/uptrain-ai/uptrain/). The idea is simple:

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