{"total":19,"rows":[{"id":"2402.05391","title":"Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey","created":"2024-02-08","cited_by_count":53,"area":null},{"id":"2504.11975","title":"SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakes","created":"2025-04-16","cited_by_count":27,"area":null},{"id":"2407.04693","title":"ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models","created":"2024-07-05","cited_by_count":14,"area":null},{"id":"2312.09785","title":"RJUA-QA: A Comprehensive QA Dataset for Urology","created":"2023-12-15","cited_by_count":6,"area":null},{"id":"2502.01812","title":"SelfCheck-Eval: A Multi-Module Framework for Zero-Resource Hallucination Detection in Large Language Models","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2412.17189","title":"Talking with Tables for Better LLM Factual Data Interactions","created":"2024-12-22","cited_by_count":1,"area":null},{"id":"2504.10167","title":"C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2509.02198","title":"FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain","created":"2025-09-02","cited_by_count":1,"area":null},{"id":"2510.19318","title":"HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2603.20252","title":"FinReflectKG -- HalluBench: GraphRAG Hallucination Benchmark for Financial Question Answering Systems","created":"2026-03-11","cited_by_count":1,"area":null},{"id":"2605.10341","title":"PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2404.01210","title":"AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis","created":"2024-04-01","cited_by_count":0,"area":null},{"id":"2509.13702","title":"DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2509.20097","title":"Integrated Framework for LLM Evaluation with Answer Generation","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2509.21104","title":"PerHalluEval: Persian Hallucination Evaluation Benchmark for Large Language Models","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2510.10539","title":"Detecting Hallucinations in Authentic LLM-Human Interactions","created":"2025-10-12","cited_by_count":0,"area":null},{"id":"2511.04643","title":"When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2511.18301","title":"\"AGI\" team at SHROOM-CAP: Data-Centric Approach to Multilingual Hallucination Detection using XLM-RoBERTa","created":"2025-11-23","cited_by_count":0,"area":null},{"id":"2608.12571","title":"Is this Citation on Point?","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}