{"total":113,"rows":[{"id":"2305.16264","title":"Scaling Data-Constrained Language Models","created":"2023-05-25","cited_by_count":403,"area":null},{"id":"2306.13213","title":"Visual Adversarial Examples Jailbreak Aligned Large Language Models","created":"2023-06-22","cited_by_count":328,"area":"mllm"},{"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":"2401.01301","title":"Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models","created":"2024-01-02","cited_by_count":150,"area":null},{"id":"2403.03883","title":"SaulLM-7B: A pioneering Large Language Model for Law","created":"2024-03-06","cited_by_count":142,"area":null},{"id":"2307.14430","title":"Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models","created":"2023-07-26","cited_by_count":121,"area":null},{"id":"2303.15715","title":"Foundation Models and Fair Use","created":"2023-03-28","cited_by_count":116,"area":null},{"id":"2210.07700","title":"Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey","created":"2022-10-14","cited_by_count":80,"area":null},{"id":"2402.17840","title":"Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems","created":"2024-02-27","cited_by_count":76,"area":"rag"},{"id":"2304.08442","title":"The MiniPile Challenge for Data-Efficient Language Models","created":"2023-04-17","cited_by_count":75,"area":null},{"id":"2308.04430","title":"SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore","created":"2023-08-08","cited_by_count":71,"area":null},{"id":"2301.13126","title":"LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain","created":"2023-01-30","cited_by_count":68,"area":null},{"id":"2405.19327","title":"MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series","created":"2024-05-29","cited_by_count":67,"area":null},{"id":"2306.02069","title":"MultiLegalPile: A 689GB Multilingual Legal Corpus","created":"2023-06-03","cited_by_count":60,"area":null},{"id":"2307.05532","title":"Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators","created":"2023-07-08","cited_by_count":56,"area":"alignment"},{"id":"2305.07507","title":"LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development","created":"2023-05-12","cited_by_count":54,"area":null},{"id":"2309.09530","title":"Adapting Large Language Models to Domains via Reading Comprehension","created":"2023-09-18","cited_by_count":50,"area":null},{"id":"2305.16157","title":"Training Data Extraction From Pre-trained Language Models: A Survey","created":"2023-05-25","cited_by_count":45,"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":"2406.01375","title":"D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models","created":"2024-06-03","cited_by_count":41,"area":null},{"id":"2209.06049","title":"Pre-trained Language Models for the Legal Domain: A Case Study on Indian Law","created":"2022-09-13","cited_by_count":39,"area":null},{"id":"2407.19584","title":"SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain","created":"2024-07-28","cited_by_count":39,"area":null},{"id":"2405.19325","title":"Nearest Neighbor Speculative Decoding for LLM Generation and Attribution","created":"2024-05-29","cited_by_count":25,"area":"inference"},{"id":"2209.13020","title":"Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans","created":"2022-09-14","cited_by_count":23,"area":null},{"id":"2404.01651","title":"NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps","created":"2024-04-02","cited_by_count":20,"area":null},{"id":"2407.07723","title":"Lossless data compression by large models","created":"2024-06-24","cited_by_count":20,"area":null},{"id":"2506.01732","title":"Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training","created":"2025-06-02","cited_by_count":20,"area":null},{"id":"2312.17276","title":"PanGu-$\\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation","created":"2023-12-27","cited_by_count":17,"area":null},{"id":"2507.23279","title":"Unveiling Super Experts in Mixture-of-Experts Large Language Models","created":"2025-07-31","cited_by_count":17,"area":"moe"},{"id":"2306.09237","title":"One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support","created":"2023-06-15","cited_by_count":16,"area":"lr"},{"id":"2408.08274","title":"BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts","created":"2024-08-15","cited_by_count":16,"area":"moe"},{"id":"2606.25996","title":"Autodata: An agentic data scientist to create high quality synthetic data","created":"2026-06-24","cited_by_count":16,"area":"agents"},{"id":"2311.14966","title":"Walking a Tightrope -- Evaluating Large Language Models in High-Risk Domains","created":"2023-11-25","cited_by_count":15,"area":null},{"id":"2404.12691","title":"Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?","created":"2024-04-19","cited_by_count":15,"area":null},{"id":"2311.08890","title":"Large Language Models are legal but they are not: Making the case for a powerful LegalLLM","created":"2023-11-15","cited_by_count":12,"area":null},{"id":"2412.13102","title":"AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark","created":"2024-12-17","cited_by_count":12,"area":null},{"id":"2210.07095","title":"Incorporating Context into Subword Vocabularies","created":"2022-10-13","cited_by_count":11,"area":null},{"id":"2406.16746","title":"The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources","created":"2024-06-24","cited_by_count":11,"area":null},{"id":"2407.06380","title":"Data, Data Everywhere: A Guide for Pretraining Dataset Construction","created":"2024-07-08","cited_by_count":11,"area":null},{"id":"2509.00761","title":"L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit","created":"2025-08-31","cited_by_count":11,"area":"agents"},{"id":"2311.12233","title":"Unifying Corroborative and Contributive Attributions in Large Language Models","created":"2023-11-20","cited_by_count":10,"area":null},{"id":"2404.02127","title":"LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain","created":"2024-04-02","cited_by_count":10,"area":null},{"id":"2502.15680","title":"Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training","created":"2025-02-21","cited_by_count":10,"area":null},{"id":"2509.09969","title":"Large Language Models Meet Legal Artificial Intelligence: A Survey","created":"2025-09-12","cited_by_count":10,"area":null},{"id":"2510.05245","title":"Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving","created":"2025-10-06","cited_by_count":10,"area":"moe"},{"id":"2305.08428","title":"Legal Extractive Summarization of U.S. Court Opinions","created":"2023-05-15","cited_by_count":9,"area":null},{"id":"2306.05431","title":"LexGPT 0.1: pre-trained GPT-J models with Pile of Law","created":"2023-06-05","cited_by_count":9,"area":null},{"id":"2406.20087","title":"ProgressGym: Alignment with a Millennium of Moral Progress","created":"2024-06-28","cited_by_count":9,"area":null},{"id":"2506.06705","title":"DivScore: Zero-Shot Detection of LLM-Generated Text in Specialized Domains","created":"2025-06-07","cited_by_count":9,"area":null},{"id":"2301.10095","title":"Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards","created":"2023-01-24","cited_by_count":8,"area":null},{"id":"2410.09527","title":"LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English","created":"2024-10-12","cited_by_count":8,"area":null},{"id":"2410.20050","title":"AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels","created":"2024-10-26","cited_by_count":8,"area":null},{"id":"2410.05309","title":"ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models through Reinforcement Learning","created":"2024-10-04","cited_by_count":7,"area":"diffusion"},{"id":"2410.22587","title":"Toxicity of the Commons: Curating Open-Source Pre-Training Data","created":"2024-10-29","cited_by_count":7,"area":null},{"id":"2311.16201","title":"Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation","created":"2023-11-27","cited_by_count":6,"area":null},{"id":"2504.07854","title":"The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models","created":"2025-04-10","cited_by_count":6,"area":null},{"id":"2409.08098","title":"The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal","created":"2024-09-12","cited_by_count":5,"area":null},{"id":"2501.00097","title":"CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions","created":"2024-12-30","cited_by_count":5,"area":null},{"id":"2504.01840","title":"LRAGE: Legal Retrieval Augmented Generation Evaluation Tool","created":"2025-04-02","cited_by_count":5,"area":"rag"},{"id":"2505.07796","title":"Learning Dynamics in Continual Pre-Training for Large Language Models","created":"2025-05-12","cited_by_count":5,"area":null},{"id":"2507.12990","title":"Teach Old SAEs New Domain Tricks with Boosting","created":"2025-07-17","cited_by_count":5,"area":"mechinterp"},{"id":"2602.06616","title":"Confundo: Learning to Generate Robust Poison for Practical RAG Systems","created":"2026-02-06","cited_by_count":5,"area":"rag"},{"id":"2408.09777","title":"Summarizing long regulatory documents with a multi-step pipeline","created":"2024-08-19","cited_by_count":4,"area":null},{"id":"2410.08731","title":"Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models","created":"2024-10-11","cited_by_count":4,"area":null},{"id":"2502.18424","title":"Compressing Language Models for Specialized Domains","created":"2025-02-25","cited_by_count":4,"area":null},{"id":"2508.08011","title":"Optimal Transport Depth Up-Scaling","created":"2025-08-11","cited_by_count":4,"area":null},{"id":"2307.11031","title":"Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification","created":"2023-07-20","cited_by_count":3,"area":null},{"id":"2310.04632","title":"Automatic Anonymization of Swiss Federal Supreme Court Rulings","created":"2023-10-07","cited_by_count":3,"area":null},{"id":"2407.09447","title":"ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts","created":"2024-07-12","cited_by_count":3,"area":"alignment"},{"id":"2411.00066","title":"Interpretable Next-token Prediction via the Generalized Induction Head","created":"2024-10-31","cited_by_count":3,"area":null},{"id":"2601.13183","title":"OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand","created":"2026-01-19","cited_by_count":3,"area":"lr"},{"id":"2210.13086","title":"Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2410.21479","title":"TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text","created":"2024-10-28","cited_by_count":2,"area":null},{"id":"2502.16357","title":"LegalBench.PT: A Benchmark for Portuguese Law","created":"2025-02-22","cited_by_count":2,"area":null},{"id":"2503.04305","title":"Computational Law: Datasets, Benchmarks, and Ontologies","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2505.02172","title":"Identifying Legal Holdings with LLMs: A Systematic Study of Performance, Scale, and Memorization","created":"2025-05-04","cited_by_count":2,"area":null},{"id":"2508.07849","title":"Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification","created":"2025-08-11","cited_by_count":2,"area":null},{"id":"2602.09514","title":"EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies","created":"2026-02-10","cited_by_count":2,"area":"agents"},{"id":"2604.23585","title":"ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection","created":"2026-04-26","cited_by_count":2,"area":null},{"id":"2608.05164","title":"Cross-Architecture Steering Transfer in Language Models: A Systematic Empirical Study","created":"2026-05-26","cited_by_count":2,"area":"mechinterp"},{"id":"2310.10141","title":"A Search for Prompts: Generating Structured Answers from Contracts","created":"2023-10-16","cited_by_count":1,"area":null},{"id":"2312.05061","title":"LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights","created":"2023-12-08","cited_by_count":1,"area":null},{"id":"2401.11852","title":"The Right Model for the Job: An Evaluation of Legal Multi-Label Classification Baselines","created":"2024-01-22","cited_by_count":1,"area":null},{"id":"2403.13681","title":"Ayn: A Tiny yet Competitive Indian Legal Language Model Pretrained from Scratch","created":"2024-03-20","cited_by_count":1,"area":null},{"id":"2406.04855","title":"The Russian Legislative Corpus","created":"2024-06-07","cited_by_count":1,"area":null},{"id":"2407.06323","title":"When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails","created":"2024-07-08","cited_by_count":1,"area":"safety"},{"id":"2410.21695","title":"CFSafety: Comprehensive Fine-grained Safety Assessment for LLMs","created":"2024-10-29","cited_by_count":1,"area":null},{"id":"2412.20468","title":"A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement","created":"2024-12-29","cited_by_count":1,"area":null},{"id":"2506.15702","title":"Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation","created":"2025-05-30","cited_by_count":1,"area":"distill"},{"id":"2507.05455","title":"ModelCitizens: Representing Community Voices in Online Safety","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2510.19723","title":"From Answers to Guidance: A Proactive Dialogue System for Legal Documents","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2510.20002","title":"Forging GEMs: Advancing Greek NLP through Quality-Based Corpus Curation","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2601.05265","title":"Cross-Document Topic-Aligned Chunking for Retrieval-Augmented Generation","created":"2025-11-08","cited_by_count":1,"area":"rag"},{"id":"2602.09570","title":"LEMUR: A Corpus for Robust Fine-Tuning of Multilingual Law Embedding Models for Retrieval","created":"2026-02-10","cited_by_count":1,"area":null},{"id":"2606.13867","title":"Muon$^p$: Muon with Fractional Spectral Powers","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2303.05383","title":"Making a Computational Attorney","created":"2023-03-07","cited_by_count":0,"area":null},{"id":"2410.00004","title":"Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization","created":"2024-09-12","cited_by_count":0,"area":"rag"},{"id":"2504.04216","title":"A Perplexity and Menger Curvature-Based Approach for Similarity Evaluation of Large Language Models","created":"2025-04-05","cited_by_count":0,"area":null},{"id":"2504.16353","title":"Transformer-Based Extraction of Statutory Definitions from the U.S. Code","created":"2025-04-23","cited_by_count":0,"area":null},{"id":"2602.21379","title":"MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation","created":"2026-02-24","cited_by_count":0,"area":null}],"next_cursor":"100"}