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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2410.01945 |
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| _version_ | 1866910006876569600 |
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| author | Fan, Yuchen Lin, Chen Zhong, Xin Zhang, Shuo Zhou, Heng Zhang, Yuchen Liang, Mingyu Xie, Chengxing Hua, Ermo Chen, Gang He, Zhizhou Huang, Cheng Ding, Ning Zhou, Bowen |
| author_facet | Fan, Yuchen Lin, Chen Zhong, Xin Zhang, Shuo Zhou, Heng Zhang, Yuchen Liang, Mingyu Xie, Chengxing Hua, Ermo Chen, Gang He, Zhizhou Huang, Cheng Ding, Ning Zhou, Bowen |
| contents | Long-Form Question Answering (LFQA) involves generating comprehensive, paragraph-level responses to open-ended questions, which poses a significant challenge for evaluation due to the richness of information and flexible response format. Existing LFQA-evaluation benchmarks often lack reference answers and are limited in size and topic coverage, reducing their reliability. To address this gap, we introduce LFQA-E, a well-constructed, multilingual, and reference-based benchmark designed to rigorously evaluate automatic metrics for LFQA. LFQA-E comprises 1618 questions and 7323 pairwise comparisons across 15 topics, drawn from diverse sources such as online queries and examination questions, thereby enabling a comprehensive assessment of evaluation metrics. We examine five categories of metrics, encompassing 17 specific methods, using LFQA-E. The results demonstrate that none of the existing automatic metrics perform comparably to human judgments, highlighting their inability to capture the dense information in long-form responses. Furthermore, we present a detailed analysis of the failure cases and the generalization capacity of these metrics, offering insights to guide the future development of LFQA evaluation methods. The benchmark and code are available at https://github.com/YuchenFan48/LFQA-E. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01945 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LFQA-E: Carefully Benchmarking Long-form QA Evaluation Fan, Yuchen Lin, Chen Zhong, Xin Zhang, Shuo Zhou, Heng Zhang, Yuchen Liang, Mingyu Xie, Chengxing Hua, Ermo Chen, Gang He, Zhizhou Huang, Cheng Ding, Ning Zhou, Bowen Computation and Language Long-Form Question Answering (LFQA) involves generating comprehensive, paragraph-level responses to open-ended questions, which poses a significant challenge for evaluation due to the richness of information and flexible response format. Existing LFQA-evaluation benchmarks often lack reference answers and are limited in size and topic coverage, reducing their reliability. To address this gap, we introduce LFQA-E, a well-constructed, multilingual, and reference-based benchmark designed to rigorously evaluate automatic metrics for LFQA. LFQA-E comprises 1618 questions and 7323 pairwise comparisons across 15 topics, drawn from diverse sources such as online queries and examination questions, thereby enabling a comprehensive assessment of evaluation metrics. We examine five categories of metrics, encompassing 17 specific methods, using LFQA-E. The results demonstrate that none of the existing automatic metrics perform comparably to human judgments, highlighting their inability to capture the dense information in long-form responses. Furthermore, we present a detailed analysis of the failure cases and the generalization capacity of these metrics, offering insights to guide the future development of LFQA evaluation methods. The benchmark and code are available at https://github.com/YuchenFan48/LFQA-E. |
| title | LFQA-E: Carefully Benchmarking Long-form QA Evaluation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.01945 |