GERE: Generative Evidence Retrieval for Fact Verification
Open accessAbstract
Fact verification (FV) is a challenging task which aims to verify a claim using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia. Most existing approaches follow a three-step pipeline framework, including document retrieval, sentence retrieval and claim verification. High-quality evidences provided by the first two steps are the foundation of the effective reasoning in the last step. Despite being important, high-quality evidences are rarely studied by existing works for FV, which often adopt the off-the-shelf models to retrieve relevant documents and sentences in an "index-retrieve-then-rank'' fashion. This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead; ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking; iii) a fixed number of sentences are selected to form the final evidence set. In this work, we proposeGERE, the first system that retrieves evidences in a generative fashion, i.e., generating the document titles as well as evidence sentence identifiers. This enables us to mitigate the aforementioned technical issues since: i) the memory and computational cost is greatly reduced because the document index is eliminated and the heavy ranking process is replaced by a light generative process; ii) the dependency between documents and that between sentences could be captured via sequential generation process; iii) the generative formulation allows us to dynamically select a precise set of relevant evidences for each claim. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines, with both time-efficiency and memory-efficiency.
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“The first category, referred to as content-derived docids, includes identifiers that are extracted directly from document elements such as titles [10, 11, 14, 25], n-grams [4, 9, 28, 29, 57], URLs [43, 68, 71, 74], and key terms [67].”
- Authors
- Chen, J., Zhang, R., Guo, J., Fan, Y., & Cheng, X.
- Affiliations
- Institute of Computing Technology (CN)
- Venue
- Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2022-07-06
- Pages
- 2184-2189
- OpenAlex
- W4223512675
- Cited by
- 66
- References
- 38
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 14 · 2023: 19 · 2022: 3
- FWCI
- 7.74
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- the Youth Innovation Promotion Association CAS (20144310, and 2021100)
- the Lenovo-CAS Joint Lab Youth Scientist Project
- the National Natural Science Foundation of China (NSFC) (62006218, 61902381, and 61872338)
- the Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission (cstc2017jcyjBX0059)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- fact-checking, FEVER dataset, Wikipedia
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