{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/fact-checking/papers/2","list_of":"/task/fact-checking","task":"Fact Checking","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":7,"rows_per_page":100,"rows":[101,200],"of":669,"counts":{"archive_papers_tagged":669,"with_a_code_link":297,"where_syntology_ran_a_sample":67,"not_listed_spam_title":0,"listed":669,"listed_where_code_ran":67,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":51,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":51,"listed_every_run_a_failure_of_syntologys_instrument":16,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/fact-checking","prev":"/task/fact-checking","next":"/task/fact-checking/papers/3","papers":[{"url":"/paper/ecis-vqg-generation-of-entity-centric","slug":"ecis-vqg-generation-of-entity-centric","title":"ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos","date":"2024-10-13","arxiv_id":"2410.09776","repositories_listed":1,"syntology":null},{"url":"/paper/take-it-easy-label-adaptive-self","slug":"take-it-easy-label-adaptive-self","title":"Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation","date":"2024-10-05","arxiv_id":"2410.04002","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/take-it-easy-label-adaptive-self#ran","syntology_url":"https://syntology.ai/paper/2410.04002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04002"}},"official":{"repos":["jingyng/label-adaptive-self-rationalization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/loki-an-open-source-tool-for-fact","slug":"loki-an-open-source-tool-for-fact","title":"Loki: An Open-Source Tool for Fact Verification","date":"2024-10-02","arxiv_id":"2410.01794","repositories_listed":1,"syntology":null},{"url":"/paper/hybridfc-a-hybrid-fact-checking-approach-for","slug":"hybridfc-a-hybrid-fact-checking-approach-for","title":"HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs","date":"2024-09-10","arxiv_id":"2409.06692","repositories_listed":1,"syntology":null},{"url":"/paper/grounding-fallacies-misrepresenting","slug":"grounding-fallacies-misrepresenting","title":"Grounding Fallacies Misrepresenting Scientific Publications in Evidence","date":"2024-08-23","arxiv_id":"2408.12812","repositories_listed":1,"syntology":null},{"url":"/paper/evidence-backed-fact-checking-using-rag-and","slug":"evidence-backed-fact-checking-using-rag-and","title":"Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs","date":"2024-08-22","arxiv_id":"2408.12060","repositories_listed":1,"syntology":null},{"url":"/paper/image-tell-me-your-story-predicting-the","slug":"image-tell-me-your-story-predicting-the","title":"\"Image, Tell me your story!\" Predicting the original meta-context of visual misinformation","date":"2024-08-19","arxiv_id":"2408.09939","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/image-tell-me-your-story-predicting-the#ran","syntology_url":"https://syntology.ai/paper/2408.09939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.09939"}},"official":{"repos":["ukplab/5pils"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/communitykg-rag-leveraging-community","slug":"communitykg-rag-leveraging-community","title":"CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking","date":"2024-08-16","arxiv_id":"2408.08535","repositories_listed":1,"syntology":null},{"url":"/paper/web-retrieval-agents-for-evidence-based","slug":"web-retrieval-agents-for-evidence-based","title":"Web Retrieval Agents for Evidence-Based Misinformation Detection","date":"2024-08-15","arxiv_id":"2409.00009","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learning-and-key-points-are-all-you","slug":"zero-shot-learning-and-key-points-are-all-you","title":"Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking","date":"2024-08-15","arxiv_id":"2408.08400","repositories_listed":1,"syntology":null},{"url":"/paper/crowd-intelligence-for-early-misinformation","slug":"crowd-intelligence-for-early-misinformation","title":"Crowd Intelligence for Early Misinformation Prediction on Social Media","date":"2024-08-08","arxiv_id":"2408.04463","repositories_listed":1,"syntology":null},{"url":"/paper/medical-graph-rag-towards-safe-medical-large","slug":"medical-graph-rag-towards-safe-medical-large","title":"Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation","date":"2024-08-08","arxiv_id":"2408.04187","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/medical-graph-rag-towards-safe-medical-large#ran","syntology_url":"https://syntology.ai/paper/2408.04187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.04187"}},"official":{"repos":["medicinetoken/medical-graph-rag"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/2407-21441","slug":"2407-21441","title":"QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications","date":"2024-07-31","arxiv_id":"2407.21441","repositories_listed":1,"syntology":null},{"url":"/paper/metasumperceiver-multimodal-multi-document","slug":"metasumperceiver-multimodal-multi-document","title":"MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking","date":"2024-07-18","arxiv_id":"2407.13089","repositories_listed":1,"syntology":null},{"url":"/paper/similarity-over-factuality-are-we-making","slug":"similarity-over-factuality-are-we-making","title":"Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?","date":"2024-07-18","arxiv_id":"2407.13488","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/similarity-over-factuality-are-we-making#ran","syntology_url":"https://syntology.ai/paper/2407.13488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13488"}},"official":{"repos":["stevejpapad/outcontext-misinfo-progress"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/flooding-spread-of-manipulated-knowledge-in","slug":"flooding-spread-of-manipulated-knowledge-in","title":"Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities","date":"2024-07-10","arxiv_id":"2407.07791","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/flooding-spread-of-manipulated-knowledge-in#ran","syntology_url":"https://syntology.ai/paper/2407.07791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07791"}},"official":{"repos":["Jometeorie/KnowledgeSpread"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/chartgemma-visual-instruction-tuning-for","slug":"chartgemma-visual-instruction-tuning-for","title":"ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild","date":"2024-07-04","arxiv_id":"2407.04172","repositories_listed":1,"syntology":null},{"url":"/paper/meerkat-audio-visual-large-language-model-for","slug":"meerkat-audio-visual-large-language-model-for","title":"Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time","date":"2024-07-01","arxiv_id":"2407.01851","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/meerkat-audio-visual-large-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2407.01851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.01851"}},"official":{"repos":["schowdhury671/meerkat"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/molecular-facts-desiderata-for","slug":"molecular-facts-desiderata-for","title":"Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification","date":"2024-06-28","arxiv_id":"2406.20079","repositories_listed":1,"syntology":null},{"url":"/paper/factfinders-at-checkthat-2024-refining-check","slug":"factfinders-at-checkthat-2024-refining-check","title":"FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning","date":"2024-06-26","arxiv_id":"2406.18297","repositories_listed":1,"syntology":null},{"url":"/paper/an-enhanced-fake-news-detection-system-with","slug":"an-enhanced-fake-news-detection-system-with","title":"An Enhanced Fake News Detection System With Fuzzy Deep Learning","date":"2024-06-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-transparency-of-machine-generated","slug":"evaluating-transparency-of-machine-generated","title":"Evaluating Evidence Attribution in Generated Fact Checking Explanations","date":"2024-06-18","arxiv_id":"2406.12645","repositories_listed":1,"syntology":null},{"url":"/paper/mfc-bench-benchmarking-multimodal-fact","slug":"mfc-bench-benchmarking-multimodal-fact","title":"MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models","date":"2024-06-17","arxiv_id":"2406.11288","repositories_listed":1,"syntology":null},{"url":"/paper/document-level-claim-extraction-and","slug":"document-level-claim-extraction-and","title":"Document-level Claim Extraction and Decontextualisation for Fact-Checking","date":"2024-06-05","arxiv_id":"2406.03239","repositories_listed":1,"syntology":null},{"url":"/paper/ratt-athought-structure-for-coherent-and","slug":"ratt-athought-structure-for-coherent-and","title":"RATT: A Thought Structure for Coherent and Correct LLM Reasoning","date":"2024-06-04","arxiv_id":"2406.02746","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/ratt-athought-structure-for-coherent-and#ran","syntology_url":"https://syntology.ai/paper/2406.02746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02746"}},"official":{"repos":["jinghanzhang1998/ratt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/factgenius-combining-zero-shot-prompting-and","slug":"factgenius-combining-zero-shot-prompting-and","title":"FactGenius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphs","date":"2024-06-03","arxiv_id":"2406.01311","repositories_listed":1,"syntology":null},{"url":"/paper/tell-me-why-explainable-public-health-fact","slug":"tell-me-why-explainable-public-health-fact","title":"Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models","date":"2024-05-15","arxiv_id":"2405.09454","repositories_listed":1,"syntology":null},{"url":"/paper/credible-unreliable-or-leaked-evidence","slug":"credible-unreliable-or-leaked-evidence","title":"Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking","date":"2024-04-29","arxiv_id":"2404.18971","repositories_listed":1,"syntology":null},{"url":"/paper/ekohate-abusive-language-and-hate-speech","slug":"ekohate-abusive-language-and-hate-speech","title":"EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter","date":"2024-04-28","arxiv_id":"2404.18180","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-retrieval-leveraging-fine","slug":"reinforcement-retrieval-leveraging-fine","title":"Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM","date":"2024-04-26","arxiv_id":"2404.17283","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/reinforcement-retrieval-leveraging-fine#ran","syntology_url":"https://syntology.ai/paper/2404.17283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.17283"}},"official":{"repos":["jadecurl/ffrr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/reliability-estimation-of-news-media-sources","slug":"reliability-estimation-of-news-media-sources","title":"Reliability Estimation of News Media Sources: Birds of a Feather Flock Together","date":"2024-04-15","arxiv_id":"2404.09565","repositories_listed":1,"syntology":null},{"url":"/paper/knowhalu-hallucination-detection-via-multi","slug":"knowhalu-hallucination-detection-via-multi","title":"KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking","date":"2024-04-03","arxiv_id":"2404.02935","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/knowhalu-hallucination-detection-via-multi#ran","syntology_url":"https://syntology.ai/paper/2404.02935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02935"}},"official":{"repos":["javyduck/knowhalu"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rematch-robust-and-efficient-matching-of","slug":"rematch-robust-and-efficient-matching-of","title":"Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity","date":"2024-04-02","arxiv_id":"2404.02126","repositories_listed":1,"syntology":null},{"url":"/paper/fact-checking-beyond-training-set","slug":"fact-checking-beyond-training-set","title":"Fact Checking Beyond Training Set","date":"2024-03-27","arxiv_id":"2403.18671","repositories_listed":1,"syntology":null},{"url":"/paper/attribute-first-then-generate-locally","slug":"attribute-first-then-generate-locally","title":"Attribute First, then Generate: Locally-attributable Grounded Text Generation","date":"2024-03-25","arxiv_id":"2403.17104","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/attribute-first-then-generate-locally#ran","syntology_url":"https://syntology.ai/paper/2403.17104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17104"}},"official":{"repos":["lovodkin93/attribute-first-then-generate"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/numtemp-a-real-world-benchmark-to-verify","slug":"numtemp-a-real-world-benchmark-to-verify","title":"QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims","date":"2024-03-25","arxiv_id":"2403.17169","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/numtemp-a-real-world-benchmark-to-verify#ran","syntology_url":"https://syntology.ai/paper/2403.17169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17169"}},"official":{"repos":["factiverse/QuanTemp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ru22fact-optimizing-evidence-for-multilingual","slug":"ru22fact-optimizing-evidence-for-multilingual","title":"RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict","date":"2024-03-25","arxiv_id":"2403.16662","repositories_listed":1,"syntology":null},{"url":"/paper/ax-to-grind-urdu-benchmark-dataset-for-urdu","slug":"ax-to-grind-urdu-benchmark-dataset-for-urdu","title":"Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection","date":"2024-03-20","arxiv_id":"2403.14037","repositories_listed":1,"syntology":null},{"url":"/paper/correcting-misinformation-on-social-media","slug":"correcting-misinformation-on-social-media","title":"Correcting misinformation on social media with a large language model","date":"2024-03-17","arxiv_id":"2403.11169","repositories_listed":1,"syntology":null},{"url":"/paper/fact-checking-the-output-of-large-language","slug":"fact-checking-the-output-of-large-language","title":"Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification","date":"2024-03-07","arxiv_id":"2403.04696","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-learning-vs-low-resource-fine","slug":"cross-lingual-learning-vs-low-resource-fine","title":"Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish","date":"2024-03-01","arxiv_id":"2403.00411","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-graph-reasoning-for-fact","slug":"heterogeneous-graph-reasoning-for-fact","title":"Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables","date":"2024-02-20","arxiv_id":"2402.13028","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/heterogeneous-graph-reasoning-for-fact#ran","syntology_url":"https://syntology.ai/paper/2402.13028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13028"}},"official":{"repos":["deno-v/heterfc"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/trustscore-reference-free-evaluation-of-llm","slug":"trustscore-reference-free-evaluation-of-llm","title":"TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness","date":"2024-02-19","arxiv_id":"2402.12545","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/trustscore-reference-free-evaluation-of-llm#ran","syntology_url":"https://syntology.ai/paper/2402.12545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12545"}},"official":{"repos":["dannalily/trustscore"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/afacta-assisting-the-annotation-of-factual","slug":"afacta-assisting-the-annotation-of-factual","title":"AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators","date":"2024-02-16","arxiv_id":"2402.11073","repositories_listed":1,"syntology":null},{"url":"/paper/entgpt-linking-generative-large-language","slug":"entgpt-linking-generative-large-language","title":"EntGPT: Linking Generative Large Language Models with Knowledge Bases","date":"2024-02-09","arxiv_id":"2402.06738","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-veracity-predictions-with-evidence","slug":"explaining-veracity-predictions-with-evidence","title":"Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach","date":"2024-02-09","arxiv_id":"2402.06443","repositories_listed":1,"syntology":null},{"url":"/paper/fact-gpt-fact-checking-augmentation-via-claim","slug":"fact-gpt-fact-checking-augmentation-via-claim","title":"FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs","date":"2024-02-08","arxiv_id":"2402.05904","repositories_listed":1,"syntology":null},{"url":"/paper/fakeclaim-a-multiple-platform-driven-dataset","slug":"fakeclaim-a-multiple-platform-driven-dataset","title":"FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War","date":"2024-01-29","arxiv_id":"2401.16625","repositories_listed":1,"syntology":null},{"url":"/paper/how-we-refute-claims-automatic-fact-checking","slug":"how-we-refute-claims-automatic-fact-checking","title":"How We Refute Claims: Automatic Fact-Checking through Flaw Identification and Explanation","date":"2024-01-27","arxiv_id":"2401.15312","repositories_listed":1,"syntology":null},{"url":"/paper/llmcheckup-conversational-examination-of","slug":"llmcheckup-conversational-examination-of","title":"LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations","date":"2024-01-23","arxiv_id":"2401.12576","repositories_listed":1,"syntology":null},{"url":"/paper/justilm-few-shot-justification-generation-for","slug":"justilm-few-shot-justification-generation-for","title":"JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims","date":"2024-01-16","arxiv_id":"2401.08026","repositories_listed":1,"syntology":null},{"url":"/paper/pipeline-and-dataset-generation-for-automated","slug":"pipeline-and-dataset-generation-for-automated","title":"Pipeline and Dataset Generation for Automated Fact-checking in Almost Any Language","date":"2023-12-15","arxiv_id":"2312.10171","repositories_listed":1,"syntology":null},{"url":"/paper/fuzzy-deep-hybrid-network-for-fake-news","slug":"fuzzy-deep-hybrid-network-for-fake-news","title":"Fuzzy Deep Hybrid Network for Fake News Detection","date":"2023-12-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/red-dot-multimodal-fact-checking-via-relevant","slug":"red-dot-multimodal-fact-checking-via-relevant","title":"RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection","date":"2023-11-16","arxiv_id":"2311.09939","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/red-dot-multimodal-fact-checking-via-relevant#ran","syntology_url":"https://syntology.ai/paper/2311.09939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09939"}},"official":{"repos":["stevejpapad/relevant-evidence-detection"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/chartcheck-an-evidence-based-fact-checking","slug":"chartcheck-an-evidence-based-fact-checking","title":"ChartCheck: Explainable Fact-Checking over Real-World Chart Images","date":"2023-11-13","arxiv_id":"2311.07453","repositories_listed":1,"syntology":null},{"url":"/paper/massive-editing-for-large-language-models-via","slug":"massive-editing-for-large-language-models-via","title":"Massive Editing for Large Language Models via Meta Learning","date":"2023-11-08","arxiv_id":"2311.04661","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/massive-editing-for-large-language-models-via#ran","syntology_url":"https://syntology.ai/paper/2311.04661","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.04661"}},"official":{"repos":["chenmientan/malmen"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-question-answering-with-reinforcement","slug":"causal-question-answering-with-reinforcement","title":"Causal Question Answering with Reinforcement Learning","date":"2023-11-05","arxiv_id":"2311.02760","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-deepfakes-without-seeing-any","slug":"detecting-deepfakes-without-seeing-any","title":"Detecting Deepfakes Without Seeing Any","date":"2023-11-02","arxiv_id":"2311.01458","repositories_listed":1,"syntology":null},{"url":"/paper/lost-in-translation-found-in-spans","slug":"lost-in-translation-found-in-spans","title":"Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media","date":"2023-10-27","arxiv_id":"2310.18205","repositories_listed":1,"syntology":null},{"url":"/paper/from-chaos-to-clarity-claim-normalization-to","slug":"from-chaos-to-clarity-claim-normalization-to","title":"From Chaos to Clarity: Claim Normalization to Empower Fact-Checking","date":"2023-10-22","arxiv_id":"2310.14338","repositories_listed":1,"syntology":null},{"url":"/paper/ask-to-the-point-open-domain-entity-centric","slug":"ask-to-the-point-open-domain-entity-centric","title":"Ask To The Point: Open-Domain Entity-Centric Question Generation","date":"2023-10-21","arxiv_id":"2310.14126","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-interactions-between-text-spans","slug":"explaining-interactions-between-text-spans","title":"Explaining Interactions Between Text Spans","date":"2023-10-20","arxiv_id":"2310.13506","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-retrieval-augmented-reader-models","slug":"optimizing-retrieval-augmented-reader-models","title":"Optimizing Retrieval-augmented Reader Models via Token Elimination","date":"2023-10-20","arxiv_id":"2310.13682","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/optimizing-retrieval-augmented-reader-models#ran","syntology_url":"https://syntology.ai/paper/2310.13682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13682"}},"official":{"repos":["mosheber/token_elimination"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fake-news-in-sheep-s-clothing-robust-fake","slug":"fake-news-in-sheep-s-clothing-robust-fake","title":"Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks","date":"2023-10-16","arxiv_id":"2310.10830","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fake-news-in-sheep-s-clothing-robust-fake#ran","syntology_url":"https://syntology.ai/paper/2310.10830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10830"}},"official":{"repos":["jiayingwu19/sheepdog"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/qacheck-a-demonstration-system-for-question","slug":"qacheck-a-demonstration-system-for-question","title":"QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking","date":"2023-10-11","arxiv_id":"2310.07609","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/qacheck-a-demonstration-system-for-question#ran","syntology_url":"https://syntology.ai/paper/2310.07609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07609"}},"official":{"repos":["xinyuanlu00/qacheck"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/x-parade-cross-lingual-textual-entailment-and","slug":"x-parade-cross-lingual-textual-entailment-and","title":"X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs","date":"2023-09-16","arxiv_id":"2309.08873","repositories_listed":1,"syntology":null},{"url":"/paper/fin-fact-a-benchmark-dataset-for-multimodal","slug":"fin-fact-a-benchmark-dataset-for-multimodal","title":"Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation","date":"2023-09-15","arxiv_id":"2309.08793","repositories_listed":1,"syntology":null},{"url":"/paper/healthfc-a-dataset-of-health-claims-for","slug":"healthfc-a-dataset-of-health-claims-for","title":"HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking","date":"2023-09-15","arxiv_id":"2309.08503","repositories_listed":1,"syntology":null},{"url":"/paper/the-calla-dataset-probing-llms-interactive","slug":"the-calla-dataset-probing-llms-interactive","title":"Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain","date":"2023-09-08","arxiv_id":"2309.04198","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-out-of-context-image-caption-pairs","slug":"detecting-out-of-context-image-caption-pairs","title":"Detecting Out-of-Context Image-Caption Pairs in News: A Counter-Intuitive Method","date":"2023-08-31","arxiv_id":"2308.16611","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-the-generation-of-fact-checking","slug":"benchmarking-the-generation-of-fact-checking","title":"Benchmarking the Generation of Fact Checking Explanations","date":"2023-08-29","arxiv_id":"2308.15202","repositories_listed":1,"syntology":null},{"url":"/paper/journey-to-the-center-of-the-knowledge","slug":"journey-to-the-center-of-the-knowledge","title":"Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons","date":"2023-08-25","arxiv_id":"2308.13198","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/journey-to-the-center-of-the-knowledge#ran","syntology_url":"https://syntology.ai/paper/2308.13198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13198"}},"official":{"repos":["heng840/amig"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/artificial-intelligence-is-ineffective-and","slug":"artificial-intelligence-is-ineffective-and","title":"Fact-checking information from large language models can decrease headline discernment","date":"2023-08-21","arxiv_id":"2308.10800","repositories_listed":1,"syntology":null},{"url":"/paper/designing-and-evaluating-presentation","slug":"designing-and-evaluating-presentation","title":"Designing and Evaluating Presentation Strategies for Fact-Checked Content","date":"2023-08-20","arxiv_id":"2308.10220","repositories_listed":1,"syntology":null},{"url":"/paper/text2kgbench-a-benchmark-for-ontology-driven","slug":"text2kgbench-a-benchmark-for-ontology-driven","title":"Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text","date":"2023-08-04","arxiv_id":"2308.02357","repositories_listed":1,"syntology":null},{"url":"/paper/mythqa-query-based-large-scale-check-worthy","slug":"mythqa-query-based-large-scale-check-worthy","title":"MythQA: Query-Based Large-Scale Check-Worthy Claim Detection through Multi-Answer Open-Domain Question Answering","date":"2023-07-21","arxiv_id":"2307.11848","repositories_listed":1,"syntology":null},{"url":"/paper/3han-a-deep-neural-network-for-fake-news","slug":"3han-a-deep-neural-network-for-fake-news","title":"3HAN: A Deep Neural Network for Fake News Detection","date":"2023-06-21","arxiv_id":"2306.12014","repositories_listed":1,"syntology":null},{"url":"/paper/reta-llm-a-retrieval-augmented-large-language","slug":"reta-llm-a-retrieval-augmented-large-language","title":"RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit","date":"2023-06-08","arxiv_id":"2306.05212","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/reta-llm-a-retrieval-augmented-large-language#ran","syntology_url":"https://syntology.ai/paper/2306.05212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05212"}},"official":{"repos":["ruc-gsai/yulan-ir"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/early-rumor-detection-using-neural-hawkes-1","slug":"early-rumor-detection-using-neural-hawkes-1","title":"Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset","date":"2023-06-05","arxiv_id":"2306.02597","repositories_listed":1,"syntology":null},{"url":"/paper/check-covid-fact-checking-covid-19-news","slug":"check-covid-fact-checking-covid-19-news","title":"Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence","date":"2023-05-29","arxiv_id":"2305.18265","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-check-worthy-claims-in-political","slug":"detecting-check-worthy-claims-in-political","title":"Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data","date":"2023-05-24","arxiv_id":"2306.05535","repositories_listed":1,"syntology":null},{"url":"/paper/overprompt-enhancing-chatgpt-capabilities","slug":"overprompt-enhancing-chatgpt-capabilities","title":"OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning","date":"2023-05-24","arxiv_id":"2305.14973","repositories_listed":1,"syntology":null},{"url":"/paper/self-checker-plug-and-play-modules-for-fact","slug":"self-checker-plug-and-play-modules-for-fact","title":"Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models","date":"2023-05-24","arxiv_id":"2305.14623","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-checker-plug-and-play-modules-for-fact#ran","syntology_url":"https://syntology.ai/paper/2305.14623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14623"}},"official":{"repos":["Miaoranmmm/SelfChecker"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-critique-prompting-with-large-language","slug":"self-critique-prompting-with-large-language","title":"Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting","date":"2023-05-23","arxiv_id":"2305.13733","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-automated-fact-checking-a-survey","slug":"multimodal-automated-fact-checking-a-survey","title":"Multimodal Automated Fact-Checking: A Survey","date":"2023-05-22","arxiv_id":"2305.13507","repositories_listed":1,"syntology":null},{"url":"/paper/scitab-a-challenging-benchmark-for","slug":"scitab-a-challenging-benchmark-for","title":"SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables","date":"2023-05-22","arxiv_id":"2305.13186","repositories_listed":1,"syntology":null},{"url":"/paper/complex-claim-verification-with-evidence","slug":"complex-claim-verification-with-evidence","title":"Complex Claim Verification with Evidence Retrieved in the Wild","date":"2023-05-19","arxiv_id":"2305.11859","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/complex-claim-verification-with-evidence#ran","syntology_url":"https://syntology.ai/paper/2305.11859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11859"}},"official":{"repos":["jifan-chen/fact-checking-via-raw-evidence"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/critic-large-language-models-can-self-correct","slug":"critic-large-language-models-can-self-correct","title":"CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing","date":"2023-05-19","arxiv_id":"2305.11738","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/critic-large-language-models-can-self-correct#ran","syntology_url":"https://syntology.ai/paper/2305.11738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11738"}},"official":{"repos":["microsoft/ProphetNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/aedfact-scientific-fact-checking-made-easier","slug":"aedfact-scientific-fact-checking-made-easier","title":"aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions","date":"2023-05-12","arxiv_id":"2305.07796","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-evaluation-of-attribution-by-large","slug":"automatic-evaluation-of-attribution-by-large","title":"Automatic Evaluation of Attribution by Large Language Models","date":"2023-05-10","arxiv_id":"2305.06311","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/automatic-evaluation-of-attribution-by-large#ran","syntology_url":"https://syntology.ai/paper/2305.06311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06311"}},"official":{"repos":["osu-nlp-group/attrscore"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/newsquote-a-dataset-built-on-quote-extraction","slug":"newsquote-a-dataset-built-on-quote-extraction","title":"NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking","date":"2023-05-05","arxiv_id":"2305.04825","repositories_listed":1,"syntology":null},{"url":"/paper/search-in-the-chain-towards-the-accurate","slug":"search-in-the-chain-towards-the-accurate","title":"Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks","date":"2023-04-28","arxiv_id":"2304.14732","repositories_listed":1,"syntology":null},{"url":"/paper/the-intended-uses-of-automated-fact-checking","slug":"the-intended-uses-of-automated-fact-checking","title":"The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who","date":"2023-04-27","arxiv_id":"2304.14238","repositories_listed":1,"syntology":null},{"url":"/paper/factify-2-a-multimodal-fake-news-and-satire","slug":"factify-2-a-multimodal-fake-news-and-satire","title":"Factify 2: A Multimodal Fake News and Satire News Dataset","date":"2023-04-08","arxiv_id":"2304.03897","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-unified-language-checking","slug":"interpretable-unified-language-checking","title":"Interpretable Unified Language Checking","date":"2023-04-07","arxiv_id":"2304.03728","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/interpretable-unified-language-checking#ran","syntology_url":"https://syntology.ai/paper/2304.03728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03728"}},"official":{"repos":["luohongyin/unilc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/selfcheckgpt-zero-resource-black-box","slug":"selfcheckgpt-zero-resource-black-box","title":"SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models","date":"2023-03-15","arxiv_id":"2303.08896","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/selfcheckgpt-zero-resource-black-box#ran","syntology_url":"https://syntology.ai/paper/2303.08896","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08896"}},"official":{"repos":["potsawee/selfcheckgpt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/bodega-benchmark-for-adversarial-example","slug":"bodega-benchmark-for-adversarial-example","title":"Verifying the Robustness of Automatic Credibility Assessment","date":"2023-03-14","arxiv_id":"2303.08032","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-based-counter","slug":"reinforcement-learning-based-counter","title":"Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation","date":"2023-03-11","arxiv_id":"2303.06433","repositories_listed":1,"syntology":null},{"url":"/paper/implicit-temporal-reasoning-for-evidence","slug":"implicit-temporal-reasoning-for-evidence","title":"Implicit Temporal Reasoning for Evidence-Based Fact-Checking","date":"2023-02-24","arxiv_id":"2302.12569","repositories_listed":1,"syntology":null},{"url":"/paper/covid-vts-fact-extraction-and-verification-on","slug":"covid-vts-fact-extraction-and-verification-on","title":"COVID-VTS: Fact Extraction and Verification on Short Video Platforms","date":"2023-02-15","arxiv_id":"2302.07919","repositories_listed":1,"syntology":null}],"record_sha256":"aff8da8f859a762717c4a9616bf9c79c7aff1141e7b5320834cde0d1e31eddb2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}