{"total":151,"rows":[{"id":"2301.13688","title":"The Flan Collection: Designing Data and Methods for Effective Instruction Tuning","created":"2023-01-31","cited_by_count":739,"area":null},{"id":"2305.17100","title":"BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks","created":"2023-05-26","cited_by_count":201,"area":"mllm"},{"id":"2403.02469","title":"Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review","created":"2024-03-04","cited_by_count":125,"area":"mllm"},{"id":"2311.05112","title":"A Survey of Large Language Models in Medicine: Progress, Application, and Challenge","created":"2023-11-09","cited_by_count":124,"area":null},{"id":"2305.14045","title":"The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning","created":"2023-05-23","cited_by_count":108,"area":"lr"},{"id":"2106.03598","title":"SciFive: a text-to-text transformer model for biomedical literature","created":"2021-05-28","cited_by_count":99,"area":null},{"id":"2302.08091","title":"Do We Still Need Clinical Language Models?","created":"2023-02-16","cited_by_count":98,"area":null},{"id":"2406.10833","title":"A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery","created":"2024-06-16","cited_by_count":96,"area":null},{"id":"2110.05006","title":"Pre-trained Language Models in Biomedical Domain: A Systematic Survey","created":"2021-10-11","cited_by_count":87,"area":null},{"id":"2502.11211","title":"A Survey of LLM-based Agents in Medicine: How far are we from Baymax?","created":"2025-02-16","cited_by_count":85,"area":"agents"},{"id":"2104.06486","title":"MS2: Multi-Document Summarization of Medical Studies","created":"2021-04-13","cited_by_count":80,"area":null},{"id":"2201.11838","title":"Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences","created":"2022-01-27","cited_by_count":69,"area":null},{"id":"2307.15051","title":"Matching Patients to Clinical Trials with Large Language Models","created":"2023-07-27","cited_by_count":69,"area":null},{"id":"2308.05342","title":"Metacognitive Prompting Improves Understanding in Large Language Models","created":"2023-08-10","cited_by_count":58,"area":null},{"id":"2305.16859","title":"Scientific Fact-Checking: A Survey of Resources and Approaches","created":"2023-05-26","cited_by_count":54,"area":null},{"id":"2104.08305","title":"Membership Inference Attack Susceptibility of Clinical Language Models","created":"2021-04-16","cited_by_count":52,"area":"safety"},{"id":"2304.14670","title":"Prompt Engineering for Healthcare: Methodologies and Applications","created":"2023-04-28","cited_by_count":52,"area":null},{"id":"2407.12994","title":"A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks","created":"2024-07-17","cited_by_count":47,"area":null},{"id":"2102.05281","title":"Biomedical Question Answering: A Survey of Approaches and Challenges","created":"2021-02-10","cited_by_count":46,"area":null},{"id":"2502.12671","title":"Baichuan-M1: Pushing the Medical Capability of Large Language Models","created":"2025-02-18","cited_by_count":43,"area":null},{"id":"2304.03086","title":"ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model","created":"2023-03-23","cited_by_count":40,"area":"mllm"},{"id":"2304.05368","title":"Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding","created":"2023-04-09","cited_by_count":40,"area":null},{"id":"2310.10628","title":"Data Contamination Through the Lens of Time","created":"2023-10-16","cited_by_count":40,"area":null},{"id":"2408.12547","title":"Towards Evaluating and Building Versatile Large Language Models for Medicine","created":"2024-08-22","cited_by_count":40,"area":null},{"id":"2303.17579","title":"Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation","created":"2023-03-29","cited_by_count":37,"area":null},{"id":"2109.02555","title":"GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain","created":"2021-09-06","cited_by_count":36,"area":null},{"id":"2302.08958","title":"Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts","created":"2023-02-17","cited_by_count":36,"area":null},{"id":"2301.11847","title":"A Comparative Study of Pretrained Language Models for Long Clinical Text","created":"2023-01-27","cited_by_count":35,"area":null},{"id":"2309.08503","title":"HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking","created":"2023-09-15","cited_by_count":35,"area":null},{"id":"2406.03712","title":"A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions","created":"2024-06-06","cited_by_count":34,"area":null},{"id":"2409.15277","title":"A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?","created":"2024-09-23","cited_by_count":33,"area":null},{"id":"2105.00827","title":"AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models","created":"2021-04-16","cited_by_count":32,"area":null},{"id":"2402.09262","title":"MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models","created":"2024-02-14","cited_by_count":29,"area":"mllm"},{"id":"2204.10290","title":"Learning to Revise References for Faithful Summarization","created":"2022-04-13","cited_by_count":28,"area":null},{"id":"2411.04118","title":"Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?","created":"2024-11-06","cited_by_count":28,"area":"mllm"},{"id":"2506.10896","title":"BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP","created":"2025-06-12","cited_by_count":28,"area":null},{"id":"2210.11892","title":"BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)","created":"2022-10-21","cited_by_count":21,"area":null},{"id":"2408.13833","title":"Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data","created":"2024-08-25","cited_by_count":21,"area":null},{"id":"2404.04963","title":"SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials","created":"2024-04-07","cited_by_count":20,"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":"2505.10717","title":"A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment","created":"2025-05-15","cited_by_count":20,"area":"alignment"},{"id":"2408.02056","title":"MedSyn: LLM-based Synthetic Medical Text Generation Framework","created":"2024-08-04","cited_by_count":19,"area":null},{"id":"2108.11696","title":"Rethinking Why Intermediate-Task Fine-Tuning Works","created":"2021-08-26","cited_by_count":18,"area":null},{"id":"2305.03598","title":"NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports","created":"2023-05-05","cited_by_count":18,"area":null},{"id":"2404.09135","title":"Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions","created":"2024-04-14","cited_by_count":18,"area":null},{"id":"2412.02621","title":"Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions","created":"2024-12-03","cited_by_count":18,"area":null},{"id":"2106.01491","title":"MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain","created":"2021-06-02","cited_by_count":17,"area":null},{"id":"2307.00067","title":"Transformers in Healthcare: A Survey","created":"2023-06-30","cited_by_count":17,"area":null},{"id":"2410.21348","title":"Large Language Model Benchmarks in Medical Tasks","created":"2024-10-28","cited_by_count":17,"area":null},{"id":"2311.09581","title":"DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation","created":"2023-11-16","cited_by_count":16,"area":null},{"id":"2401.00579","title":"Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing","created":"2023-12-31","cited_by_count":16,"area":null},{"id":"2210.14814","title":"BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples","created":"2022-10-26","cited_by_count":15,"area":null},{"id":"2302.04725","title":"Lightweight Transformers for Clinical Natural Language Processing","created":"2023-02-09","cited_by_count":15,"area":"distill"},{"id":"2107.04374","title":"Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT","created":"2021-07-09","cited_by_count":14,"area":null},{"id":"2109.04810","title":"Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT","created":"2021-09-10","cited_by_count":14,"area":null},{"id":"2209.14901","title":"DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing","created":"2022-09-29","cited_by_count":14,"area":"lr"},{"id":"2303.03948","title":"A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization","created":"2023-03-07","cited_by_count":14,"area":null},{"id":"2204.03035","title":"Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding","created":"2022-04-06","cited_by_count":13,"area":null},{"id":"2212.10784","title":"Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?","created":"2022-12-21","cited_by_count":13,"area":null},{"id":"2404.04067","title":"Does Biomedical Training Lead to Better Medical Performance?","created":"2024-04-05","cited_by_count":13,"area":null},{"id":"2405.06275","title":"Pruning as a Domain-specific LLM Extractor","created":"2024-05-10","cited_by_count":13,"area":null},{"id":"2405.05135","title":"Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks","created":"2024-04-24","cited_by_count":12,"area":null},{"id":"2307.06439","title":"Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events","created":"2023-07-12","cited_by_count":11,"area":"distill"},{"id":"2106.07285","title":"Probing Pre-Trained Language Models for Disease Knowledge","created":"2021-06-14","cited_by_count":10,"area":null},{"id":"2112.05780","title":"A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing","created":"2021-12-07","cited_by_count":10,"area":null},{"id":"2201.06499","title":"RuMedBench: A Russian Medical Language Understanding Benchmark","created":"2022-01-17","cited_by_count":10,"area":null},{"id":"2402.12749","title":"Me LLaMA: Foundation Large Language Models for Medical Applications","created":"2024-02-20","cited_by_count":10,"area":null},{"id":"2304.13180","title":"Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports","created":"2023-04-25","cited_by_count":9,"area":null},{"id":"2210.06566","title":"Developing a general-purpose clinical language inference model from a large corpus of clinical notes","created":"2022-10-12","cited_by_count":8,"area":null},{"id":"2405.11877","title":"A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus","created":"2024-05-20","cited_by_count":8,"area":null},{"id":"2605.20270","title":"Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs","created":"2026-05-18","cited_by_count":8,"area":null},{"id":"2111.08529","title":"Improving the robustness and accuracy of biomedical language models through adversarial training","created":"2021-11-16","cited_by_count":7,"area":null},{"id":"2305.02993","title":"SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data","created":"2023-05-04","cited_by_count":7,"area":null},{"id":"2307.10188","title":"Several categories of Large Language Models (LLMs): A Short Survey","created":"2023-07-05","cited_by_count":7,"area":null},{"id":"2311.16075","title":"BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights","created":"2023-11-27","cited_by_count":7,"area":null},{"id":"2108.12242","title":"Deep learning models are not robust against noise in clinical text","created":"2021-08-27","cited_by_count":6,"area":null},{"id":"2111.01340","title":"Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks","created":"2021-11-02","cited_by_count":6,"area":null},{"id":"2204.02922","title":"Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding","created":"2022-04-06","cited_by_count":6,"area":null},{"id":"2205.12181","title":"Partial-input baselines show that NLI models can ignore context, but they don't","created":"2022-05-24","cited_by_count":6,"area":null},{"id":"2209.06453","title":"Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words","created":"2022-09-14","cited_by_count":6,"area":null},{"id":"2303.13386","title":"Compositional Zero-Shot Domain Transfer with Text-to-Text Models","created":"2023-03-23","cited_by_count":6,"area":null},{"id":"2406.13131","title":"When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models","created":"2024-06-19","cited_by_count":6,"area":null},{"id":"2408.11735","title":"Clinical Insights: A Comprehensive Review of Language Models in Medicine","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2505.17436","title":"Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning","created":"2025-05-23","cited_by_count":6,"area":"mllm"},{"id":"2101.10196","title":"A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts","created":"2021-01-25","cited_by_count":5,"area":null},{"id":"2103.09635","title":"SILT: Efficient transformer training for inter-lingual inference","created":"2021-03-17","cited_by_count":5,"area":null},{"id":"2104.05847","title":"Targeted Adversarial Training for Natural Language Understanding","created":"2021-04-12","cited_by_count":5,"area":null},{"id":"2210.06456","title":"Are Sample-Efficient NLP Models More Robust?","created":"2022-10-12","cited_by_count":5,"area":null},{"id":"2407.01948","title":"Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation","created":"2024-07-02","cited_by_count":5,"area":null},{"id":"2412.12422","title":"FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs","created":"2024-12-17","cited_by_count":5,"area":null},{"id":"2504.20547","title":"Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records","created":"2025-04-29","cited_by_count":5,"area":null},{"id":"2507.07419","title":"MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning","created":"2025-07-10","cited_by_count":5,"area":null},{"id":"2510.16458","title":"Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations","created":"2025-10-18","cited_by_count":5,"area":null},{"id":"2511.12693","title":"HEDGE: Hallucination Estimation via Dense Geometric Entropy for VQA with Vision-Language Models","created":"2025-11-16","cited_by_count":5,"area":"mllm"},{"id":"2603.19532","title":"EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Models","created":"2026-03-20","cited_by_count":5,"area":null},{"id":"2207.02093","title":"Predicting Out-of-Domain Generalization with Neighborhood Invariance","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2209.13953","title":"ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection","created":"2022-09-28","cited_by_count":4,"area":null},{"id":"2210.05598","title":"Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation","created":"2022-10-11","cited_by_count":4,"area":null},{"id":"2306.01245","title":"THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2412.14426","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","created":"2024-12-19","cited_by_count":4,"area":null}],"next_cursor":"100"}