{"total":59,"rows":[{"id":"2405.16533","title":"Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents","created":"2024-05-26","cited_by_count":56,"area":"agents"},{"id":"2411.02959","title":"HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems","created":"2024-11-05","cited_by_count":37,"area":"rag"},{"id":"2407.17023","title":"DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models","created":"2024-07-24","cited_by_count":34,"area":null},{"id":"2501.12835","title":"Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home","created":"2025-01-22","cited_by_count":33,"area":"rag"},{"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":"2409.18786","title":"A Survey on the Honesty of Large Language Models","created":"2024-09-27","cited_by_count":23,"area":"alignment"},{"id":"2403.19216","title":"Are Large Language Models Good at Utility Judgments?","created":"2024-03-28","cited_by_count":22,"area":"rag"},{"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":"2411.14572","title":"Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective","created":"2024-11-21","cited_by_count":20,"area":"rag"},{"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":"2502.15799","title":"Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models","created":"2025-02-18","cited_by_count":18,"area":"lr"},{"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":"2402.12219","title":"Reformatted Alignment","created":"2024-02-19","cited_by_count":15,"area":"alignment"},{"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":"2410.02507","title":"Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration","created":"2024-10-03","cited_by_count":14,"area":"lr"},{"id":"2505.20871","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","created":"2025-05-27","cited_by_count":14,"area":"rag"},{"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":"2510.14337","title":"Stop-RAG: Value-Based Retrieval Control for Iterative RAG","created":"2025-10-16","cited_by_count":14,"area":"rag"},{"id":"2405.18727","title":"CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control","created":"2024-05-29","cited_by_count":13,"area":"rag"},{"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":"2604.17112","title":"Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification","created":"2026-04-18","cited_by_count":10,"area":"lr"},{"id":"2412.12767","title":"A Survey of Calibration Process for Black-Box LLMs","created":"2024-12-17","cited_by_count":8,"area":null},{"id":"2509.06461","title":"Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning","created":"2025-09-08","cited_by_count":8,"area":"lr"},{"id":"2511.09980","title":"Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG","created":"2025-11-13","cited_by_count":8,"area":"rag"},{"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":"2502.14100","title":"Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach","created":"2025-02-19","cited_by_count":6,"area":"rag"},{"id":"2505.12731","title":"Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps","created":"2025-05-19","cited_by_count":6,"area":"rag"},{"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":"2601.06842","title":"Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation","created":"2026-01-11","cited_by_count":6,"area":"rag"},{"id":"2604.02543","title":"Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation","created":"2026-04-02","cited_by_count":6,"area":"mllm"},{"id":"2510.11407","title":"KnowRL: Teaching Language Models to Know What They Know","created":"2025-10-13","cited_by_count":5,"area":null},{"id":"2506.07309","title":"ConfRAG: Confidence-Guided Retrieval-Augmenting Generation","created":"2025-06-08","cited_by_count":4,"area":"rag"},{"id":"2606.19950","title":"Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA","created":"2026-06-18","cited_by_count":4,"area":"mllm"},{"id":"2506.06240","title":"Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge","created":"2025-06-06","cited_by_count":3,"area":"rag"},{"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":"2410.08320","title":"Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation","created":"2024-10-10","cited_by_count":2,"area":"rag"},{"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":"2601.14896","title":"Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation","created":"2026-01-21","cited_by_count":2,"area":"lr"},{"id":"2603.12149","title":"Linking Perception, Confidence and Accuracy in MLLMs","created":"2026-03-12","cited_by_count":2,"area":"mllm"},{"id":"2604.23333","title":"Process Supervision of Confidence Margin for Calibrated LLM Reasoning","created":"2026-04-25","cited_by_count":2,"area":"lr"},{"id":"2501.02842","title":"Foundations of GenIR","created":"2025-01-06","cited_by_count":1,"area":null},{"id":"2508.05650","title":"OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools","created":"2025-07-26","cited_by_count":1,"area":"rag"},{"id":"2512.09148","title":"Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment","created":"2025-12-09","cited_by_count":1,"area":"rag"},{"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":"2406.10263","title":"A Lightweight Framework for Adaptive Retrieval In Code Completion With Critique Model","created":"2024-06-11","cited_by_count":0,"area":"rag"},{"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":"2509.06472","title":"Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking","created":"2025-09-08","cited_by_count":0,"area":"lr"},{"id":"2511.03261","title":"Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature","created":"2025-11-05","cited_by_count":0,"area":"rag"},{"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}