{"total":61,"rows":[{"id":"2404.13076","title":"LLM Evaluators Recognize and Favor Their Own Generations","created":"2024-04-15","cited_by_count":686,"area":"alignment"},{"id":"2411.18279","title":"Large Language Model-Brained GUI Agents: A Survey","created":"2024-11-27","cited_by_count":162,"area":"agents"},{"id":"2506.09038","title":"AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions","created":"2025-06-10","cited_by_count":145,"area":"lr"},{"id":"2407.18418","title":"Know Your Limits: A Survey of Abstention in Large Language Models","created":"2024-07-25","cited_by_count":121,"area":null},{"id":"2405.20974","title":"SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales","created":"2024-05-31","cited_by_count":105,"area":null},{"id":"2407.12043","title":"The Art of Saying No: Contextual Noncompliance in Language Models","created":"2024-07-02","cited_by_count":105,"area":null},{"id":"2504.17550","title":"HalluLens: LLM Hallucination Benchmark","created":"2025-04-24","cited_by_count":92,"area":null},{"id":"2309.06794","title":"Cognitive Mirage: A Review of Hallucinations in Large Language Models","created":"2023-09-13","cited_by_count":84,"area":null},{"id":"2312.07000","title":"Alignment for Honesty","created":"2023-12-12","cited_by_count":74,"area":"alignment"},{"id":"2504.06514","title":"Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?","created":"2025-04-09","cited_by_count":58,"area":"lr"},{"id":"2404.10960","title":"Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations","created":"2024-04-16","cited_by_count":54,"area":null},{"id":"2404.12452","title":"Characterizing LLM Abstention Behavior in Science QA with Context Perturbations","created":"2024-04-18","cited_by_count":26,"area":null},{"id":"2409.18786","title":"A Survey on the Honesty of Large Language Models","created":"2024-09-27","cited_by_count":23,"area":"alignment"},{"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":"2501.03151","title":"Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches","created":"2025-01-06","cited_by_count":18,"area":"mllm"},{"id":"2401.06659","title":"WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge","created":"2024-01-12","cited_by_count":16,"area":null},{"id":"2606.28733","title":"Agentic Abstention: Do Agents Know When to Stop Instead of Act?","created":"2026-06-27","cited_by_count":15,"area":"agents"},{"id":"2412.06748","title":"Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models","created":"2024-12-09","cited_by_count":14,"area":null},{"id":"2503.19168","title":"Language Model Uncertainty Quantification with Attention Chain","created":"2025-03-24","cited_by_count":14,"area":null},{"id":"2506.09669","title":"Query-Level Uncertainty in Large Language Models","created":"2025-06-11","cited_by_count":14,"area":null},{"id":"2509.22449","title":"Detecting (Un)answerability in Large Language Models with Linear Directions","created":"2025-09-26","cited_by_count":13,"area":null},{"id":"2502.19573","title":"Do Large Language Models Know How Much They Know?","created":"2025-02-26","cited_by_count":11,"area":null},{"id":"2505.23224","title":"MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration","created":"2025-05-29","cited_by_count":11,"area":"lr"},{"id":"2601.05503","title":"Over-Searching in Search-Augmented Large Language Models","created":"2026-01-09","cited_by_count":11,"area":null},{"id":"2506.05451","title":"Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety","created":"2025-06-05","cited_by_count":10,"area":"safety"},{"id":"2504.16939","title":"A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions","created":"2025-04-07","cited_by_count":9,"area":"agents"},{"id":"2505.03467","title":"Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis","created":"2025-05-06","cited_by_count":9,"area":null},{"id":"2510.10390","title":"RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models","created":"2025-10-12","cited_by_count":9,"area":"rag"},{"id":"2403.09085","title":"Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance","created":"2024-03-14","cited_by_count":8,"area":"lr"},{"id":"2412.12527","title":"When to Speak, When to Abstain: Contrastive Decoding with Abstention","created":"2024-12-17","cited_by_count":8,"area":null},{"id":"2502.13329","title":"Language Models Can Predict Their Own Behavior","created":"2025-02-18","cited_by_count":8,"area":"lr"},{"id":"2604.18419","title":"Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning","created":"2026-04-20","cited_by_count":8,"area":"lr"},{"id":"2507.02870","title":"Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models","created":"2025-06-06","cited_by_count":7,"area":null},{"id":"2510.02279","title":"Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation","created":"2025-10-02","cited_by_count":7,"area":null},{"id":"2605.00737","title":"To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling","created":"2026-05-01","cited_by_count":7,"area":"agents"},{"id":"2310.08840","title":"Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue","created":"2023-10-13","cited_by_count":6,"area":null},{"id":"2410.20199","title":"Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models","created":"2024-10-26","cited_by_count":6,"area":"lr"},{"id":"2501.01457","title":"Beyond Introspection: Reinforcing Thinking via Externalist Behavioral Feedback","created":"2024-12-31","cited_by_count":5,"area":"lr"},{"id":"2308.14353","title":"ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models","created":"2023-08-28","cited_by_count":4,"area":null},{"id":"2605.20544","title":"The Yes-Man Syndrome: Benchmarking Abstention in Embodied Robotic Agents","created":"2026-05-19","cited_by_count":4,"area":"mllm"},{"id":"2604.10718","title":"SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?","created":"2026-04-12","cited_by_count":3,"area":null},{"id":"2607.05752","title":"When Should LLMs Search? Counterfactual Supervision for Search Routing","created":"2026-07-07","cited_by_count":3,"area":null},{"id":"2605.20098","title":"Neurosymbolic Learning for Inference-Time Argumentation","created":"2026-05-19","cited_by_count":2,"area":null},{"id":"2608.01358","title":"HopRefusalBench: Diagnosing Refusal Failures in Search-Augmented Agents for Multi-Hop Reasoning","created":"2026-08-02","cited_by_count":2,"area":"lr"},{"id":"2609.07879","title":"Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty","created":"2026-09-07","cited_by_count":2,"area":null},{"id":"2510.16340","title":"Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models","created":"2025-10-18","cited_by_count":1,"area":"lr"},{"id":"2601.22648","title":"UCPO: Uncertainty-Aware Policy Optimization","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2602.02582","title":"Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems","created":"2026-01-31","cited_by_count":1,"area":null},{"id":"2604.02830","title":"GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2606.00869","title":"Enhancing LLM Metacognition via Cognitive Pairwise Training","created":"2026-05-30","cited_by_count":1,"area":"lr"},{"id":"2408.06736","title":"Speculations on Uncertainty and Humane Algorithms","created":"2024-08-13","cited_by_count":0,"area":null},{"id":"2512.21238","title":"Assessing the Software Security Comprehension of Large Language Models","created":"2025-12-24","cited_by_count":0,"area":null},{"id":"2601.22786","title":"Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Framework","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2603.27033","title":"RealBirdID: Benchmarking Bird Species Identification in the Era of MLLMs","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2607.14250","title":"The Severance Problem: LLMs are Unaware of the Person Beyond the Prompt","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.22138","title":"Measuring Stability and Failure Behavior in Language Models Under Structured Perturbations","created":"2026-08-22","cited_by_count":0,"area":null},{"id":"2608.27924","title":"What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?","created":"2026-08-28","cited_by_count":0,"area":"agents"},{"id":"2609.02771","title":"From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.17516","title":"When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.32964","title":"The Commit-Abstain Circuit: Why Language Models Hallucinate Instead of Abstaining","created":"2026-09-26","cited_by_count":0,"area":"mechinterp"},{"id":"2610.01006","title":"Beyond Answer Confidence: A Controlled Audit of Self-Knowledge in a Black-Box Decision Model","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}