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arXiv · 2306.01200

Multi-Dimensional Evaluation of Text Summarization with In-Context Learning

Sameer Jain·Vaishakh Keshava·Swarnashree Mysore Sathyendra·Patrick Fernandes·Pengfei Liu·Graham Neubig·Chunting Zhou
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Abstract

Evaluation of natural language generation (NLG) is complex and multi-dimensional. Generated text can be evaluated for fluency, coherence, factuality, or any other dimensions of interest. Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets. In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning, obviating the need for large training datasets. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization, establishing state-of-the-art on dimensions such as relevance and factual consistency. We then analyze the effects of factors such as the selection and number of in-context examples on performance. Finally, we study the efficacy of in-context learning based evaluators in evaluating zero-shot summaries written by large language models such as GPT-3.

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Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig, Chunting Zhou. 2023-06-01. Multi-Dimensional Evaluation of Text Summarization with In-Context Learning. https://doi.org/10.18653/v1%2F2023.findings-acl.537

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