Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization

• URL: http://arxiv.org/abs/2310.08394v2
• Date: Fri, 20 Oct 2023 10:42:59 GMT
• Title: Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization
• Authors: Ondrej Skopek, Rahul Aralikatte, Sian Gooding, Victor Carbune
• Abstract summary: We perform a meta-evaluation of a variety of metrics to quantify how accurately they measure the instruction-following abilities of large language models. Using riSum, we analyze the agreement between evaluation methods and human judgment.
• Score: 9.686937153317809
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite recent advances, evaluating how well large language models (LLMs) follow user instructions remains an open problem. While evaluation methods of language models have seen a rise in prompt-based approaches, limited work on the correctness of these methods has been conducted. In this work, we perform a meta-evaluation of a variety of metrics to quantify how accurately they measure the instruction-following abilities of LLMs. Our investigation is performed on grounded query-based summarization by collecting a new short-form, real-world dataset riSum, containing 300 document-instruction pairs with 3 answers each. All 900 answers are rated by 3 human annotators. Using riSum, we analyze the agreement between evaluation methods and human judgment. Finally, we propose new LLM-based reference-free evaluation methods that improve upon established baselines and perform on par with costly reference-based metrics that require high-quality summaries.

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