{"total":18,"rows":[{"id":"2410.23609","title":"On Positional Bias of Faithfulness for Long-form Summarization","created":"2024-10-31","cited_by_count":22,"area":null},{"id":"2506.01829","title":"CiteEval: Principle-Driven Citation Evaluation for Source Attribution","created":"2025-06-02","cited_by_count":19,"area":null},{"id":"2409.08435","title":"When Context Leads but Parametric Memory Follows in Large Language Models","created":"2024-09-13","cited_by_count":11,"area":null},{"id":"2502.06185","title":"Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long Document Summarization","created":"2025-02-10","cited_by_count":8,"area":null},{"id":"2404.12041","title":"A Survey of Automatic Hallucination Evaluation on Natural Language Generation","created":"2024-04-18","cited_by_count":7,"area":null},{"id":"2406.14709","title":"Factual Dialogue Summarization via Learning from Large Language Models","created":"2024-06-20","cited_by_count":6,"area":null},{"id":"2505.15291","title":"Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization","created":"2025-05-21","cited_by_count":6,"area":null},{"id":"2404.03344","title":"Schroedinger's Threshold: When the AUC doesn't predict Accuracy","created":"2024-04-04","cited_by_count":4,"area":null},{"id":"2505.16518","title":"CUB: Benchmarking Context Utilisation Techniques for Language Models","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2507.20858","title":"A survey of diversity quantification in natural language processing: The why, what, where and how","created":"2025-07-28","cited_by_count":4,"area":null},{"id":"2505.21786","title":"VeriTrail: Closed-Domain Hallucination Detection with Traceability","created":"2025-05-27","cited_by_count":3,"area":null},{"id":"2505.14101","title":"MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2510.21118","title":"The Gray Zone of Faithfulness: Taming Ambiguity in Unfaithfulness Detection","created":"2025-10-24","cited_by_count":1,"area":null},{"id":"2601.06666","title":"InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs","created":"2026-01-10","cited_by_count":1,"area":null},{"id":"2604.16686","title":"No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation","created":"2026-04-17","cited_by_count":1,"area":null},{"id":"2408.00675","title":"Leveraging Entailment Judgements in Cross-Lingual Summarisation","created":"2024-08-01","cited_by_count":0,"area":null},{"id":"2607.09932","title":"Faithful by Design: Evaluating and Improving LLM-Generated Clinical Trial Summaries for Multi-Stakeholder Audiences","created":"2026-07-10","cited_by_count":0,"area":null},{"id":"2608.00205","title":"Averaging Bias: Human Faithfulness Annotations are not Locally Faithful","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}