{"total":28,"rows":[{"id":"2407.17023","title":"DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models","created":"2024-07-24","cited_by_count":34,"area":null},{"id":"2502.11677","title":"Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception","created":"2025-02-17","cited_by_count":27,"area":null},{"id":"2408.09773","title":"Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?","created":"2024-08-19","cited_by_count":23,"area":null},{"id":"2405.14383","title":"Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering","created":"2024-05-23","cited_by_count":20,"area":null},{"id":"2412.17032","title":"MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge","created":"2024-12-22","cited_by_count":20,"area":null},{"id":"2404.00216","title":"Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness","created":"2024-03-30","cited_by_count":17,"area":null},{"id":"2503.13804","title":"Empowering GraphRAG with Knowledge Filtering and Integration","created":"2025-03-18","cited_by_count":15,"area":null},{"id":"2404.01574","title":"Multi-granular Adversarial Attacks against Black-box Neural Ranking Models","created":"2024-04-02","cited_by_count":14,"area":null},{"id":"2507.10587","title":"Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing","created":"2025-07-11","cited_by_count":14,"area":null},{"id":"2510.09033","title":"Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness","created":"2025-10-10","cited_by_count":10,"area":null},{"id":"2512.24661","title":"Do Large Language Models Know What They Are Capable Of?","created":"2025-12-31","cited_by_count":10,"area":null},{"id":"2412.12767","title":"A Survey of Calibration Process for Black-Box LLMs","created":"2024-12-17","cited_by_count":8,"area":null},{"id":"2503.11256","title":"Line of Duty: Evaluating LLM Self-Knowledge via Consistency in Feasibility Boundaries","created":"2025-03-14","cited_by_count":7,"area":null},{"id":"2504.20084","title":"AI Awareness","created":"2025-04-25","cited_by_count":7,"area":null},{"id":"2406.14023","title":"Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective","created":"2024-06-20","cited_by_count":6,"area":null},{"id":"2505.17537","title":"Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity","created":"2025-05-23","cited_by_count":6,"area":null},{"id":"2510.11407","title":"KnowRL: Teaching Language Models to Know What They Know","created":"2025-10-13","cited_by_count":5,"area":null},{"id":"2510.17509","title":"Annotation-Efficient Universal Honesty Alignment","created":"2025-10-20","cited_by_count":3,"area":null},{"id":"2602.07842","title":"Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers","created":"2026-02-08","cited_by_count":3,"area":null},{"id":"2506.18998","title":"Mirage of Mastery: Memorization Tricks LLMs into Artificially Inflated Self-Knowledge","created":"2025-06-23","cited_by_count":2,"area":null},{"id":"2501.02842","title":"Foundations of GenIR","created":"2025-01-06","cited_by_count":1,"area":null},{"id":"2603.09654","title":"Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2604.10189","title":"FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness","created":"2026-04-11","cited_by_count":1,"area":null},{"id":"2606.03535","title":"Can LLM Rerankers Predict Their Own Ranking Performance?","created":"2026-06-02","cited_by_count":1,"area":null},{"id":"2412.11536","title":"Let your LLM generate a few tokens and you will reduce the need for retrieval","created":"2024-12-16","cited_by_count":0,"area":null},{"id":"2505.21849","title":"Xinyu AI Search: Enhanced Relevance and Comprehensive Results with Rich Answer Presentations","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2608.14683","title":"One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2609.33721","title":"Shared Experience, Separate Learning: Companion Confidence Calibration for LLMs","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}