{"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/factcg-enhancing-fact-checkers-with-graph","title":"FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data","arxiv_id":"2501.17144","date":"2025-01-28","proceeding":null,"authors":["Deren Lei","Yaxi Li","SiYao Li","Mengya Hu","Rui Xu","Ken Archer","Mingyu Wang","Emily Ching","Alex Deng"],"abstract":"Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detecting LLM hallucinations. Recent approaches to document-level synthetic data generation involve iteratively removing sentences from documents and annotating factuality using LLM-based prompts. While effective, this method is computationally expensive for long documents and limited by the LLM's capabilities. In this work, we analyze the differences between existing synthetic training data used in state-of-the-art models and real LLM output claims. Based on our findings, we propose a novel approach for synthetic data generation, CG2C, that leverages multi-hop reasoning on context graphs extracted from documents. Our fact checker model, FactCG, demonstrates improved performance with more connected reasoning, using the same backbone models. Experiments show it even outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size.","url_abs":"https://arxiv.org/abs/2501.17144v1","url_pdf":"https://arxiv.org/pdf/2501.17144v1.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":"factcg-enhancing-fact-checkers-with-graph","repo_url":"https://github.com/derenlei/factcg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2501.17144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.17144"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/derenlei/FactCG","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c1798c50ae7165bb","entry":"get_scores","repo":"derenlei/FactCG","repo_kind":"official","path":"benchmark.py","file_url":"https://github.com/derenlei/FactCG/blob/HEAD/benchmark.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c1798c50ae7165bb"}},{"code_sha256_prefix":"bec4d78405f6ec00","entry":"get_scores","repo":"derenlei/FactCG","repo_kind":"official","path":"wice_connected_reasoning.py","file_url":"https://github.com/derenlei/FactCG/blob/HEAD/wice_connected_reasoning.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bec4d78405f6ec00"}},{"code_sha256_prefix":"758353db78d553c4","entry":"get_threshold","repo":"derenlei/FactCG","repo_kind":"official","path":"benchmark.py","file_url":"https://github.com/derenlei/FactCG/blob/HEAD/benchmark.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"758353db78d553c4"}},{"code_sha256_prefix":"d5dae5b168198604","entry":"run_testset","repo":"derenlei/FactCG","repo_kind":"official","path":"benchmark.py","file_url":"https://github.com/derenlei/FactCG/blob/HEAD/benchmark.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d5dae5b168198604"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}