On the Evaluation of Machine-Generated Reports
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929338896613376 |
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| author | Mayfield, James Yang, Eugene Lawrie, Dawn MacAvaney, Sean McNamee, Paul Oard, Douglas W. Soldaini, Luca Soboroff, Ian Weller, Orion Kayi, Efsun Sanders, Kate Mason, Marc Hibbler, Noah |
| author_facet | Mayfield, James Yang, Eugene Lawrie, Dawn MacAvaney, Sean McNamee, Paul Oard, Douglas W. Soldaini, Luca Soboroff, Ian Weller, Orion Kayi, Efsun Sanders, Kate Mason, Marc Hibbler, Noah |
| contents | 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 related research areas, to present our vision for automatic report generation, and -- critically -- a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable -- if not required -- in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Evaluation of Machine-Generated Reports Mayfield, James Yang, Eugene Lawrie, Dawn MacAvaney, Sean McNamee, Paul Oard, Douglas W. Soldaini, Luca Soboroff, Ian Weller, Orion Kayi, Efsun Sanders, Kate Mason, Marc Hibbler, Noah Computation and Language Information Retrieval 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 related research areas, to present our vision for automatic report generation, and -- critically -- a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable -- if not required -- in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability. |
| title | On the Evaluation of Machine-Generated Reports |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2405.00982 |