# ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs arXiv: https://arxiv.org/abs/2403.09724 ยท HTML: https://mlsanity.com/paper/2403.09724/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 54 references that are in the map. - First posted on arXiv 12 Mar 2024. ## Builds on (most cited first, 30 of 30) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022) - [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022) - [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023) - [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020) - [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018) - [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023) - [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023) - [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020) - [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023) - [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017) - [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019) - [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023) - [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022) - [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021) - [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020) - [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017) - [SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling](https://mlsanity.com/paper/2312.15166/) (2023) - [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021) - [Simple Entity-Centric Questions Challenge Dense Retrievers](https://mlsanity.com/paper/2109.08535/) (2021) - [Entailment as Few-Shot Learner](https://mlsanity.com/paper/2104.14690/) (2021) - [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://mlsanity.com/paper/2212.08037/) (2022) - [Automatic Evaluation of Attribution by Large Language Models](https://mlsanity.com/paper/2305.06311/) (2023) - [Language Models as Fact Checkers?](https://mlsanity.com/paper/2006.04102/) (2020) - [Language Models Hallucinate, but May Excel at Fact Verification](https://mlsanity.com/paper/2310.14564/) (2023) - [SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)](https://mlsanity.com/paper/2105.13995/) (2021) ## Built on by (most cited first, 11 of 11) - [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024) - [Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science](https://mlsanity.com/paper/2506.04410/) (2025) - [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025) - [CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models](https://mlsanity.com/paper/2510.02342/) (2025) - [ClaimDB: A Fact Verification Benchmark over Large Structured Data](https://mlsanity.com/paper/2601.14698/) (2026) - [On Synthesizing Data for Context Attribution in Question Answering](https://mlsanity.com/paper/2504.05317/) (2025) - [Bonsai: Interpretable Tree-Adaptive Grounded Reasoning](https://mlsanity.com/paper/2504.03640/) (2025) - [Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs](https://mlsanity.com/paper/2511.12817/) (2025) - [Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosis](https://mlsanity.com/paper/2511.17947/) (2025) - [LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval](https://mlsanity.com/paper/2604.18913/) (2026) - [ASTRA - Agentic System for Ticket Resolution and Analysis](https://mlsanity.com/paper/2608.28790/) (2026) ## Abstract (arXiv) In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter. Many fact-checking approaches and tools have been developed, but they often lack appropriate explainability or granularity to be useful in various contexts. A text validation method that is easy to use, accessible, and can perform fine-grained evidence attribution has become crucial. More importantly, building user trust in such a method requires presenting the rationale behind each prediction, as research shows this significantly influences people's belief in automated systems. Localizing and bringing users' attention to the specific problematic content is also paramount, instead of providing simple blanket labels. In this paper, we present ClaimVer, a human-centric framework tailored to meet users' informational and verification needs by generating rich annotations and thereby reducing cognitive load. Designed to deliver comprehensive evaluations of texts, it highlights each claim, verifies it against a trusted knowledge graph (KG), presents the evidence, and provides succinct, clear explanations for each claim prediction. Finally, our framework introduces an attribution score, enhancing applicability across a wide range of downstream tasks.