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On the Evaluation of Machine-Generated Reports
May 3, 2024, 4:14 a.m. | James Mayfield, Eugene Yang, Dawn Lawrie, Sean MacAvaney, Paul McNamee, Douglas W. Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Kayi, Kate S
cs.CL updates on arXiv.org arxiv.org
Abstract: Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of …
abstract arxiv cs.cl cs.ir document evaluation form generated information language language models large language large language models llms machine ranking reports struggle text text generation them type
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