{"total":93,"rows":[{"id":"2405.20362","title":"Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools","created":"2024-05-30","cited_by_count":176,"area":null},{"id":"2409.05746","title":"LLMs Will Always Hallucinate, and We Need to Live With This","created":"2024-09-09","cited_by_count":78,"area":null},{"id":"2510.06265","title":"Large Language Models Hallucination: A Comprehensive Survey","created":"2025-10-05","cited_by_count":49,"area":null},{"id":"2410.21306","title":"Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges","created":"2024-10-25","cited_by_count":42,"area":null},{"id":"2402.05200","title":"Are LLMs Ready for Real-World Materials Discovery?","created":"2024-02-07","cited_by_count":40,"area":null},{"id":"2502.08554","title":"Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies","created":"2025-02-12","cited_by_count":39,"area":null},{"id":"2504.00025","title":"Generalization Bias in Large Language Model Summarization of Scientific Research","created":"2025-03-28","cited_by_count":30,"area":null},{"id":"2504.05559","title":"SciSciGPT: Advancing Human-AI Collaboration in the Science of Science","created":"2025-04-07","cited_by_count":27,"area":null},{"id":"2409.12180","title":"Finetuning Language Models to Emit Linguistic Expressions of Uncertainty","created":"2024-09-18","cited_by_count":26,"area":null},{"id":"2502.00290","title":"Estimating LLM Uncertainty with Evidence","created":"2025-02-01","cited_by_count":26,"area":null},{"id":"2410.18860","title":"DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations","created":"2024-10-24","cited_by_count":25,"area":null},{"id":"2406.13261","title":"BeHonest: Benchmarking Honesty in Large Language Models","created":"2024-06-19","cited_by_count":24,"area":null},{"id":"2401.07348","title":"Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity","created":"2024-01-14","cited_by_count":21,"area":null},{"id":"2402.02008","title":"How well do LLMs cite relevant medical references? An evaluation framework and analyses","created":"2024-02-03","cited_by_count":21,"area":null},{"id":"2405.12701","title":"OLAPH: Improving Factuality in Biomedical Long-form Question Answering","created":"2024-05-21","cited_by_count":20,"area":null},{"id":"2505.16690","title":"Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator","created":"2025-05-22","cited_by_count":20,"area":null},{"id":"2509.00710","title":"On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representations","created":"2025-08-31","cited_by_count":18,"area":null},{"id":"2602.05930","title":"Compound Deception in Elite Peer Review: A Failure Mode Taxonomy of 100 Fabricated Citations at NeurIPS 2025","created":"2026-02-05","cited_by_count":15,"area":null},{"id":"2410.07053","title":"Robots in the Middle: Evaluating LLMs in Dispute Resolution","created":"2024-10-09","cited_by_count":14,"area":null},{"id":"2409.09041","title":"Acceptable Use Policies for Foundation Models","created":"2024-08-29","cited_by_count":12,"area":null},{"id":"2409.11798","title":"The Factuality of Large Language Models in the Legal Domain","created":"2024-09-18","cited_by_count":10,"area":null},{"id":"2510.06105","title":"Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences","created":"2025-10-07","cited_by_count":8,"area":null},{"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":"2602.04755","title":"When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?","created":"2026-02-04","cited_by_count":7,"area":null},{"id":"2404.00600","title":"AI Act and Large Language Models (LLMs): When critical issues and privacy impact require human and ethical oversight","created":"2024-03-31","cited_by_count":6,"area":null},{"id":"2410.21195","title":"Belief in the Machine: Investigating Epistemological Blind Spots of Language Models","created":"2024-10-28","cited_by_count":6,"area":null},{"id":"2501.17830","title":"A Comprehensive Survey on Legal Summarization: Challenges and Future Directions","created":"2025-01-29","cited_by_count":6,"area":null},{"id":"2510.01474","title":"AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance","created":"2025-10-01","cited_by_count":6,"area":null},{"id":"2510.24222","title":"HACK: Hallucinations Along Certainty and Knowledge Axes","created":"2025-10-28","cited_by_count":6,"area":null},{"id":"2407.06798","title":"It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human","created":"2024-07-09","cited_by_count":5,"area":null},{"id":"2410.09904","title":"Equitable Access to Justice: Logical LLMs Show Promise","created":"2024-10-13","cited_by_count":5,"area":null},{"id":"2501.13951","title":"A Layered Multi-Expert Framework for Long-Context Mental Health Assessments","created":"2025-01-20","cited_by_count":4,"area":null},{"id":"2503.01688","title":"When an LLM is apprehensive about its answers -- and when its uncertainty is justified","created":"2025-03-03","cited_by_count":4,"area":null},{"id":"2510.05126","title":"Generalization of Fine-Tuned Uncertainty Communication and Metacognition in Large Language Models","created":"2025-09-30","cited_by_count":4,"area":null},{"id":"2410.04949","title":"Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2410.17210","title":"Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2412.04576","title":"Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs","created":"2024-12-05","cited_by_count":3,"area":null},{"id":"2505.02763","title":"Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following","created":"2025-05-05","cited_by_count":3,"area":null},{"id":"2505.18597","title":"LLMs for Supply Chain Management","created":"2025-05-24","cited_by_count":3,"area":null},{"id":"2508.20040","title":"Model Science: getting serious about verification, explanation and control of AI systems","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2511.06700","title":"Place Matters: Comparing LLM Hallucination Rates for Place-Based Legal Queries","created":"2025-11-10","cited_by_count":3,"area":null},{"id":"2512.08946","title":"Large Language Models as Search Engines: Societal Challenges","created":"2025-11-24","cited_by_count":3,"area":null},{"id":"2603.05881","title":"Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation","created":"2026-03-06","cited_by_count":3,"area":null},{"id":"2605.17028","title":"PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifacts","created":"2026-05-16","cited_by_count":3,"area":null},{"id":"2407.20248","title":"LAPIS: Language Model-Augmented Police Investigation System","created":"2024-07-19","cited_by_count":2,"area":null},{"id":"2510.16712","title":"The Chameleon Nature of LLMs: Quantifying Multi-Turn Stance Instability in Search-Enabled Language Models","created":"2025-10-19","cited_by_count":2,"area":null},{"id":"2604.22779","title":"KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning","created":"2026-04-03","cited_by_count":2,"area":null},{"id":"2606.01189","title":"The Case for Model Science: Verify, Explore, Steer, Refine","created":"2026-05-31","cited_by_count":2,"area":null},{"id":"2606.19868","title":"A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models","created":"2026-06-18","cited_by_count":2,"area":null},{"id":"2609.17546","title":"Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant","created":"2026-07-15","cited_by_count":2,"area":null},{"id":"2601.05879","title":"Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law","created":"2026-01-09","cited_by_count":1,"area":null},{"id":"2601.15511","title":"AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domains","created":"2026-01-21","cited_by_count":1,"area":null},{"id":"2603.04982","title":"Training for Technology: Adoption and Productive Use of Generative AI in Legal Analysis","created":"2026-03-05","cited_by_count":1,"area":null},{"id":"2606.00898","title":"Citation Grounding Measures the Oracle: Graph Coverage Determines Reported LLM Hallucination Rates in Law","created":"2026-05-30","cited_by_count":1,"area":null},{"id":"2606.08932","title":"From Statute to Control Flow: Span-Grounded Deontic Trees for Defeasible Scope Parsing","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2606.15646","title":"NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models","created":"2026-04-05","cited_by_count":1,"area":null},{"id":"2608.20220","title":"InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries","created":"2026-08-20","cited_by_count":1,"area":null},{"id":"2509.21473","title":"Are Hallucinations Bad Estimations?","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2510.17463","title":"Label Indeterminacy in AI & Law","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2512.05681","title":"Retrieving Semantically Similar Decisions under Noisy Institutional Labels: Robust Comparison of Embedding Methods","created":"2025-12-05","cited_by_count":0,"area":null},{"id":"2512.22568","title":"Lessons from Neuroscience for AI: How integrating Actions, Compositional Structure and Episodic Memory could enable Safe, Interpretable and Human-Like AI","created":"2025-12-27","cited_by_count":0,"area":null},{"id":"2602.17671","title":"AI Hallucination from Students' Perspective: A Thematic Analysis","created":"2026-01-11","cited_by_count":0,"area":null},{"id":"2603.05828","title":"HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2603.16292","title":"Attention-guided Evidence Grounding for Spoken Question Answering","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2603.20531","title":"Epistemic Observability in Language Models","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2604.26233","title":"Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2605.01486","title":"MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation","created":"2026-05-02","cited_by_count":0,"area":null},{"id":"2605.02010","title":"Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2605.05403","title":"When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.12201","title":"Uncertainty Quantification for LLM-based Code Generation","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2606.02971","title":"EURO-5K: When Does Domain Pretraining Matter? Benchmarking Transformers for EU Reporting Obligation Extraction","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.11166","title":"Flaws in the LLM Automation Narrative","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.15893","title":"BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2606.16214","title":"Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.19334","title":"Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.21595","title":"Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2606.24585","title":"LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2606.32004","title":"PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.11127","title":"Do LLMs Fabricate Legal Citations? A Bilingual Benchmark on Saudi Data Protection Law and the GDPR","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.14105","title":"Automatically Evolving Prompt Guidelines for Task-Specific Optimization","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2607.21547","title":"The Boundaries of Automation: A Theory of Persistent Human Participation","created":"2026-07-23","cited_by_count":0,"area":null},{"id":"2607.24449","title":"Evaluating RAG for French immigration law: a benchmark and baseline study","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2608.00042","title":"Trustworthiness Costs of Domain Adaptation in Small Language Models:A Cross-Architecture Empirical Study","created":"2026-07-23","cited_by_count":0,"area":null},{"id":"2608.04641","title":"AI Literacy for Legal Translation: Developing Digital Resilience","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.08709","title":"AI Evaluation Should Measure Verification Cost, Not Correctness Alone","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2608.12571","title":"Is this Citation on Point?","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.14210","title":"How Much Do Legal RAG Systems Still Hallucinate?","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.20391","title":"ImmigrationReason: A Structured Dataset of U.S. Immigration Appeals for Legal Reasoning Research","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2608.21409","title":"Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2608.27102","title":"LAAF: A Layered Accountability Architecture Framework for LLM Applications","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.10293","title":"GANDR: Claim Auditing for Verifiable Legal Answer Generation","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.22127","title":"Beyond Accuracy and Surface Fluency: Risk-Sensitive Evaluation of LLMs for Legal Clause Generation","created":"2026-08-22","cited_by_count":0,"area":null},{"id":"2610.03639","title":"Do Large Language Models Know Colombian Law? A Reliability Benchmark for the Colombian Legal System","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}