{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flashcheck-exploration-of-efficient-evidence","title":"FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking","arxiv_id":"2502.05803","date":"2025-02-09","proceeding":null,"authors":["Kevin Nanekhan","Venktesh V","Erik Martin","Henrik Vatndal","Vinay Setty","Avishek Anand"],"abstract":"The advances in digital tools have led to the rampant spread of misinformation. While fact-checking aims to combat this, manual fact-checking is cumbersome and not scalable. It is essential for automated fact-checking to be efficient for aiding in combating misinformation in real-time and at the source. Fact-checking pipelines primarily comprise a knowledge retrieval component which extracts relevant knowledge to fact-check a claim from large knowledge sources like Wikipedia and a verification component. The existing works primarily focus on the fact-verification part rather than evidence retrieval from large data collections, which often face scalability issues for practical applications such as live fact-checking. In this study, we address this gap by exploring various methods for indexing a succinct set of factual statements from large collections like Wikipedia to enhance the retrieval phase of the fact-checking pipeline. We also explore the impact of vector quantization to further improve the efficiency of pipelines that employ dense retrieval approaches for first-stage retrieval. We study the efficiency and effectiveness of the approaches on fact-checking datasets such as HoVer and WiCE, leveraging Wikipedia as the knowledge source. We also evaluate the real-world utility of the efficient retrieval approaches by fact-checking 2024 presidential debate and also open source the collection of claims with corresponding labels identified in the debate. Through a combination of indexed facts together with Dense retrieval and Index compression, we achieve up to a 10.0x speedup on CPUs and more than a 20.0x speedup on GPUs compared to the classical fact-checking pipelines over large collections.","url_abs":"https://arxiv.org/abs/2502.05803v2","url_pdf":"https://arxiv.org/pdf/2502.05803v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"flashcheck-exploration-of-efficient-evidence","repo_url":"https://github.com/kevin-rn/Efficient-Fact-checking","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"fact-verification","task_name":"Fact Verification"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"focus","method_name":"Focus"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}