{"total":142,"rows":[{"id":"2404.16789","title":"Continual Learning of Large Language Models: A Comprehensive Survey","created":"2024-04-25","cited_by_count":234,"area":null},{"id":"2405.10825","title":"Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities","created":"2024-05-17","cited_by_count":83,"area":"lr"},{"id":"2407.13193","title":"Retrieval-Augmented Generation for Natural Language Processing: A Survey","created":"2024-07-18","cited_by_count":71,"area":"rag"},{"id":"2409.07431","title":"Synthetic continued pretraining","created":"2024-09-11","cited_by_count":60,"area":null},{"id":"2406.17975","title":"SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)","created":"2024-06-25","cited_by_count":59,"area":"safety"},{"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":"2407.09424","title":"TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models","created":"2024-07-12","cited_by_count":40,"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":"2406.11149","title":"GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory","created":"2024-06-17","cited_by_count":37,"area":null},{"id":"2407.21065","title":"LawLLM: Law Large Language Model for the US Legal System","created":"2024-07-27","cited_by_count":37,"area":null},{"id":"2508.21148","title":"A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers","created":"2025-08-28","cited_by_count":37,"area":"agents"},{"id":"2504.21039","title":"Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report","created":"2025-04-28","cited_by_count":31,"area":null},{"id":"2507.05639","title":"ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?","created":"2025-07-08","cited_by_count":27,"area":"agents"},{"id":"2503.05683","title":"WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs","created":"2025-03-07","cited_by_count":26,"area":null},{"id":"2409.05314","title":"Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications","created":"2024-09-09","cited_by_count":25,"area":null},{"id":"2501.04961","title":"Demystifying Domain-adaptive Post-training for Financial LLMs","created":"2025-01-09","cited_by_count":24,"area":null},{"id":"2407.17467","title":"CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models","created":"2024-07-24","cited_by_count":23,"area":null},{"id":"2502.11191","title":"Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training","created":"2025-02-16","cited_by_count":23,"area":"distill"},{"id":"2508.01059","title":"Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report","created":"2025-08-01","cited_by_count":23,"area":null},{"id":"2411.02335","title":"Sparsing Law: Towards Large Language Models with Greater Activation Sparsity","created":"2024-11-04","cited_by_count":22,"area":null},{"id":"2406.19465","title":"Can Large Language Models Generate High-quality Patent Claims?","created":"2024-06-27","cited_by_count":19,"area":null},{"id":"2502.10990","title":"FinMTEB: Finance Massive Text Embedding Benchmark","created":"2025-02-16","cited_by_count":18,"area":null},{"id":"2406.10303","title":"A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations","created":"2024-06-14","cited_by_count":16,"area":null},{"id":"2508.09874","title":"Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models","created":"2025-08-13","cited_by_count":16,"area":null},{"id":"2409.18511","title":"Do We Need Domain-Specific Embedding Models? An Empirical Investigation","created":"2024-09-27","cited_by_count":12,"area":null},{"id":"2504.21202","title":"Automatic Legal Writing Evaluation of LLMs","created":"2025-04-29","cited_by_count":12,"area":null},{"id":"2408.03092","title":"Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement","created":"2024-08-06","cited_by_count":11,"area":null},{"id":"2501.14431","title":"Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains","created":"2025-01-24","cited_by_count":11,"area":"lr"},{"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":"2409.11798","title":"The Factuality of Large Language Models in the Legal Domain","created":"2024-09-18","cited_by_count":10,"area":null},{"id":"2506.10424","title":"SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks","created":"2025-06-12","cited_by_count":10,"area":"safety"},{"id":"2509.09969","title":"Large Language Models Meet Legal Artificial Intelligence: A Survey","created":"2025-09-12","cited_by_count":10,"area":null},{"id":"2501.09706","title":"Domain Adaptation of Foundation LLMs for e-Commerce","created":"2025-01-16","cited_by_count":9,"area":null},{"id":"2503.01887","title":"When Continue Learning Meets Multimodal Large Language Model: A Survey","created":"2025-02-27","cited_by_count":9,"area":"mllm"},{"id":"2508.03080","title":"ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts","created":"2025-08-05","cited_by_count":9,"area":null},{"id":"2510.26309","title":"GraphCompliance: Structure-Aware Regulatory Compliance through Policy-Context Graph Alignment","created":"2025-10-30","cited_by_count":9,"area":null},{"id":"2507.21134","title":"TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law","created":"2025-07-22","cited_by_count":8,"area":"safety"},{"id":"2407.16615","title":"Lawma: The Power of Specialization for Legal Annotation","created":"2024-07-23","cited_by_count":7,"area":null},{"id":"2409.04475","title":"Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation","created":"2024-09-05","cited_by_count":7,"area":null},{"id":"2503.00841","title":"A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences","created":"2025-03-02","cited_by_count":7,"area":"lr"},{"id":"2508.21589","title":"Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning","created":"2025-08-29","cited_by_count":7,"area":null},{"id":"2509.08824","title":"Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corpora","created":"2025-09-10","cited_by_count":7,"area":null},{"id":"2409.13721","title":"LegiLM: A Fine-Tuned Legal Language Model for Data Compliance","created":"2024-09-09","cited_by_count":6,"area":null},{"id":"2410.15653","title":"Opportunities and Challenges of Generative-AI in Finance","created":"2024-10-21","cited_by_count":6,"area":null},{"id":"2412.11063","title":"LAW: Legal Agentic Workflows for Custody and Fund Services Contracts","created":"2024-12-15","cited_by_count":6,"area":"agents"},{"id":"2505.11031","title":"OntoURL: A Benchmark for Evaluating Large Language Models on Symbolic Ontological Understanding, Reasoning and Learning","created":"2025-05-16","cited_by_count":6,"area":"lr"},{"id":"2505.12950","title":"GuRE:Generative Query REwriter for Legal Passage Retrieval","created":"2025-05-19","cited_by_count":6,"area":null},{"id":"2507.10605","title":"RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services","created":"2025-07-13","cited_by_count":6,"area":null},{"id":"2508.05534","title":"CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation","created":"2025-08-07","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":"2403.18140","title":"Juru: Legal Brazilian Large Language Model from Reputable Sources","created":"2024-03-26","cited_by_count":5,"area":null},{"id":"2406.14887","title":"InternLM-Law: An Open Source Chinese Legal Large Language Model","created":"2024-06-21","cited_by_count":5,"area":null},{"id":"2408.07137","title":"ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice","created":"2024-08-13","cited_by_count":5,"area":null},{"id":"2501.12535","title":"How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?","created":"2025-01-21","cited_by_count":5,"area":null},{"id":"2502.11546","title":"DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection","created":"2025-02-17","cited_by_count":5,"area":null},{"id":"2504.04949","title":"L3AC: Towards a Lightweight and Lossless Audio Codec","created":"2025-04-07","cited_by_count":5,"area":null},{"id":"2505.12568","title":"Enriching Patent Claim Generation with European Patent Dataset","created":"2025-05-18","cited_by_count":5,"area":null},{"id":"2602.00642","title":"LegalOne: A Family of Foundation Models for Reliable Legal Reasoning","created":"2026-01-31","cited_by_count":5,"area":"lr"},{"id":"2605.28120","title":"LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning","created":"2026-05-27","cited_by_count":5,"area":"lr"},{"id":"2410.13352","title":"LAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights","created":"2024-10-17","cited_by_count":4,"area":"lr"},{"id":"2412.02549","title":"Patent-CR: A Dataset for Patent Claim Revision","created":"2024-12-03","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":"2602.09003","title":"Data Science and Technology Towards AGI Part I: Tiered Data Management","created":"2026-02-09","cited_by_count":4,"area":null},{"id":"2408.06709","title":"Review Learning: Advancing All-in-One Ultra-High-Definition Image Restoration Training Method","created":"2024-08-13","cited_by_count":3,"area":null},{"id":"2410.10181","title":"Scalable Multi-Domain Adaptation of Language Models using Modular Experts","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2410.18957","title":"Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code","created":"2024-10-24","cited_by_count":3,"area":null},{"id":"2411.00042","title":"Improving Math Problem Solving in Large Language Models Through Categorization and Strategy Tailoring","created":"2024-10-29","cited_by_count":3,"area":null},{"id":"2508.15213","title":"Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering","created":"2025-08-21","cited_by_count":3,"area":null},{"id":"2602.06359","title":"Training Data Selection with Gradient Orthogonality for Efficient Domain Adaptation","created":"2026-02-06","cited_by_count":3,"area":"lr"},{"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":"2412.15902","title":"On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Education","created":"2024-12-20","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":"2503.08379","title":"JurisTCU: A Brazilian Portuguese Information Retrieval Dataset with Query Relevance Judgments","created":"2025-03-11","cited_by_count":2,"area":null},{"id":"2504.03931","title":"NAACL2025 Tutorial: Adaptation of Large Language Models","created":"2025-04-04","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":"2510.22931","title":"Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining","created":"2025-10-27","cited_by_count":2,"area":"synth"},{"id":"2601.03725","title":"EDCO: Dynamic Curriculum Orchestration for Domain-specific Large Language Model Fine-tuning","created":"2026-01-07","cited_by_count":2,"area":null},{"id":"2603.11772","title":"Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents","created":"2026-03-12","cited_by_count":2,"area":"rag"},{"id":"2605.14057","title":"Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2605.25920","title":"Can LLMs Time Travel? Enhancing Temporal Consistency in Legal Agentic Search through Reinforcement Learning","created":"2026-05-25","cited_by_count":2,"area":"agents"},{"id":"2606.09389","title":"LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks","created":"2026-06-08","cited_by_count":2,"area":null},{"id":"2606.11105","title":"PhantomBench: Benchmarking the Non-existential Threat of Language Models","created":"2026-06-09","cited_by_count":2,"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":"2406.11555","title":"Input Conditioned Graph Generation for Language Agents","created":"2024-06-17","cited_by_count":1,"area":"agents"},{"id":"2410.09083","title":"Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment","created":"2024-10-06","cited_by_count":1,"area":null},{"id":"2412.06272","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","created":"2024-12-09","cited_by_count":1,"area":null},{"id":"2412.14771","title":"ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine","created":"2024-12-19","cited_by_count":1,"area":null},{"id":"2501.00069","title":"Adversarial Negotiation Dynamics in Generative Language Models","created":"2024-12-29","cited_by_count":1,"area":null},{"id":"2501.02068","title":"The interplay between domain specialization and model size","created":"2025-01-03","cited_by_count":1,"area":null},{"id":"2503.10676","title":"Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2503.20797","title":"\"Whose Side Are You On?\" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2506.00608","title":"PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements","created":"2025-05-31","cited_by_count":1,"area":null},{"id":"2506.07658","title":"From Raw Corpora to Domain Benchmarks: Automated Evaluation of LLM Domain Expertise","created":"2025-06-09","cited_by_count":1,"area":null},{"id":"2509.00141","title":"Scaling Legal AI: Benchmarking Mamba and Transformers for Statutory Classification and Case Law Retrieval","created":"2025-08-29","cited_by_count":1,"area":null},{"id":"2509.14750","title":"Enhancing Retrieval Augmentation via Adversarial Collaboration","created":"2025-09-18","cited_by_count":1,"area":"rag"},{"id":"2511.03563","title":"ASVRI-Legal: Fine-Tuning LLMs with Retrieval Augmented Generation for Enhanced Legal Regulation","created":"2025-11-05","cited_by_count":1,"area":"rag"},{"id":"2511.07070","title":"RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services","created":"2025-11-10","cited_by_count":1,"area":"lr"},{"id":"2603.08022","title":"Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization","created":"2026-03-09","cited_by_count":1,"area":null},{"id":"2603.22973","title":"Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisions","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2605.05532","title":"A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction","created":"2026-05-07","cited_by_count":1,"area":null}],"next_cursor":"100"}