{"total":29,"rows":[{"id":"2406.01297","title":"When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs","created":"2024-06-03","cited_by_count":240,"area":null},{"id":"2506.07962","title":"Correlated Errors in Large Language Models","created":"2025-06-09","cited_by_count":45,"area":null},{"id":"2406.13439","title":"Finding Blind Spots in Evaluator LLMs with Interpretable Checklists","created":"2024-06-19","cited_by_count":32,"area":null},{"id":"2407.17468","title":"WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries","created":"2024-07-24","cited_by_count":26,"area":null},{"id":"2510.11822","title":"Beyond Consensus: Mitigating the Agreeableness Bias in LLM Judge Evaluations","created":"2025-10-13","cited_by_count":16,"area":null},{"id":"2504.17004","title":"(Im)possibility of Automated Hallucination Detection in Large Language Models","created":"2025-04-23","cited_by_count":15,"area":null},{"id":"2511.21843","title":"FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers","created":"2025-11-26","cited_by_count":14,"area":null},{"id":"2410.12071","title":"Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products","created":"2024-10-15","cited_by_count":11,"area":null},{"id":"2502.11771","title":"The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It","created":"2025-02-17","cited_by_count":10,"area":null},{"id":"2410.12207","title":"Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?","created":"2024-10-16","cited_by_count":8,"area":null},{"id":"2506.00309","title":"Evaluation of LLMs for mathematical problem solving","created":"2025-05-30","cited_by_count":7,"area":null},{"id":"2502.05467","title":"Position: LLMs Can be Good Tutors in English Education","created":"2025-02-08","cited_by_count":6,"area":null},{"id":"2601.15812","title":"ErrorMap and ErrorAtlas: Charting the Failure Landscape of Large Language Models","created":"2026-01-22","cited_by_count":6,"area":null},{"id":"2411.18444","title":"Is my Meeting Summary Good? Estimating Quality with a Multi-LLM Evaluator","created":"2024-11-27","cited_by_count":4,"area":null},{"id":"2511.01014","title":"IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation","created":"2025-11-02","cited_by_count":4,"area":null},{"id":"2507.08459","title":"Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework","created":"2025-07-11","cited_by_count":3,"area":null},{"id":"2410.20791","title":"From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap","created":"2024-10-28","cited_by_count":2,"area":null},{"id":"2411.15387","title":"From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set","created":"2024-11-23","cited_by_count":2,"area":null},{"id":"2502.12565","title":"Self Iterative Label Refinement via Robust Unlabeled Learning","created":"2025-02-18","cited_by_count":2,"area":null},{"id":"2506.00064","title":"Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2602.05692","title":"MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations","created":"2026-02-05","cited_by_count":2,"area":null},{"id":"2510.08624","title":"Do LLMs Know They Are Being Tested? Evaluation Awareness and Incentive-Sensitive Failures in GPT-OSS-20B","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2606.13892","title":"Crypto x AI, AI x Crypto: A Survey","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2610.01471","title":"When Does a Second Model Help? Cross-Model Review in LLM Verification","created":"2026-10-01","cited_by_count":1,"area":null},{"id":"2504.07733","title":"DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China","created":"2025-04-10","cited_by_count":0,"area":null},{"id":"2505.10640","title":"The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)","created":"2025-05-15","cited_by_count":0,"area":null},{"id":"2510.03527","title":"Sample, Align, Synthesize: Graph-Based Response Synthesis with ConGrs","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2609.00918","title":"RPCBench: A Benchmark for Proactive Premise Critique in LLM-based Recommendation","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.39037","title":"Hard-Gate Candidacy in a Deployed Validator Suite","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}