short-paper

Towards Robust QA Evaluation via Open LLMs

Authors:

Ehsan Kamalloo,

Shivani Upadhyay,

Jimmy LinAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2811 - 2816

https://doi.org/10.1145/3626772.3657675

Published: 11 July 2024 Publication History

Abstract

Instruction-tuned large language models (LLMs) have been shown to be viable surrogates for the widely used, albeit overly rigid, lexical matching metrics in evaluating question answering (QA) models. However, these LLM-based evaluation methods are invariably based on proprietary LLMs. Despite their remarkable capabilities, proprietary LLMs are costly and subject to internal changes that can affect their output, which inhibits the reproducibility of their results and limits the widespread adoption of LLM-based evaluation. In this demo, we aim to use publicly available LLMs for standardizing LLM-based QA evaluation. However, open-source LLMs lag behind their proprietary counterparts. We overcome this gap by adopting chain-of-thought prompting with self-consistency to build a reliable evaluation framework. We demonstrate that our evaluation framework, based on 750M and 7B open LLMs, correlates competitively with human judgment, compared to most recent GPT-3 and GPT-4 models. Our codebase and data are available at https://github.com/castorini/qa-eval.

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Cited By

BADUROWICZ MSKULIMOWSKI SLASKOWSKI M(2024)FEASIBILITY OF USING LOW-PARAMETER LOCAL LLMS IN ANSWERING QUESTIONS FROM ENTERPRISE KNOWLEDGE BASEApplied Computer Science10.35784/acs-2024-4620:4(175-191)Online publication date: 31-Dec-2024
https://doi.org/10.35784/acs-2024-46

Index Terms

Towards Robust QA Evaluation via Open LLMs
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Retrieval tasks and goals
      1. Question answering

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

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Published: 11 July 2024

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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

Washington DC, USA

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Cited By

BADUROWICZ MSKULIMOWSKI SLASKOWSKI M(2024)FEASIBILITY OF USING LOW-PARAMETER LOCAL LLMS IN ANSWERING QUESTIONS FROM ENTERPRISE KNOWLEDGE BASEApplied Computer Science10.35784/acs-2024-4620:4(175-191)Online publication date: 31-Dec-2024
https://doi.org/10.35784/acs-2024-46

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