WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations

Haolin Deng, Chang Wang, Li Xin, Dezhang Yuan, Junlang Zhan, Tian Zhou, Jin Ma, Jun Gao, Ruifeng Xu


Abstract
Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. However, existing datasets and evaluation methods in this domain still exhibit notable limitations. In this work, we formulate the task of attributed query-focused summarization (AQFS) and present WebCiteS, a Chinese dataset featuring 7k human-annotated summaries with citations. WebCiteS derives from real-world user queries and web search results, offering a valuable resource for model training and evaluation. Prior works in attribution evaluation do not differentiate between groundedness errors and citation errors. They also fall short in automatically verifying sentences that draw partial support from multiple sources. We tackle these issues by developing detailed metrics and enabling the automatic evaluator to decompose the sentences into sub-claims for fine-grained verification. Our comprehensive evaluation of both open-source and proprietary models on WebCiteS highlights the challenge LLMs face in correctly citing sources, underscoring the necessity for further improvement. The dataset and code will be open-sourced to facilitate further research in this crucial field.
Anthology ID:
2024.acl-long.806
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15095–15114
Language:
URL:
https://aclanthology.org/2024.acl-long.806
DOI:
10.18653/v1/2024.acl-long.806
Bibkey:
Cite (ACL):
Haolin Deng, Chang Wang, Li Xin, Dezhang Yuan, Junlang Zhan, Tian Zhou, Jin Ma, Jun Gao, and Ruifeng Xu. 2024. WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15095–15114, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations (Deng et al., ACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.acl-long.806.pdf