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Evaluating Large Language Models with fmeval

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arxiv 2407.12872 v1 pith:DKPQ3FQ3 submitted 2024-07-15 cs.CL cs.LG

Pola Schwöbel , Luca Franceschi , Muhammad Bilal Zafar , Keerthan Vasist , Aman Malhotra , Tomer Shenhar , Pinal Tailor , Pinar Yilmaz
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Michael Diamond Michele Donini
This is my paper · ORCID
classification cs.CLcs.LG
keywords fmevallibrarycaseevaluatelanguagelargemodelmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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fmeval is an open source library to evaluate large language models (LLMs) in a range of tasks. It helps practitioners evaluate their model for task performance and along multiple responsible AI dimensions. This paper presents the library and exposes its underlying design principles: simplicity, coverage, extensibility and performance. We then present how these were implemented in the scientific and engineering choices taken when developing fmeval. A case study demonstrates a typical use case for the library: picking a suitable model for a question answering task. We close by discussing limitations and further work in the development of the library. fmeval can be found at https://github.com/aws/fmeval.

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  1. The Science of Evaluating Foundation Models

    cs.CL 2025-02 conditional novelty 3.0 of 10

    A survey-and-checklist proposal that organizes LLM evaluation into an ABCD framework (Algorithm, Big Data, Computation, Domain Expertise) for context-aware, documented assessment.

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