{"total":81,"rows":[{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576,"area":null},{"id":"2304.01373","title":"Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling","created":"2023-04-03","cited_by_count":1786,"area":null},{"id":"2301.00234","title":"A Survey on In-context Learning","created":"2022-12-31","cited_by_count":721,"area":null},{"id":"2211.08411","title":"Large Language Models Struggle to Learn Long-Tail Knowledge","created":"2022-11-15","cited_by_count":458,"area":null},{"id":"2305.16264","title":"Scaling Data-Constrained Language Models","created":"2023-05-25","cited_by_count":403,"area":null},{"id":"2311.06668","title":"In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering","created":"2023-11-11","cited_by_count":245,"area":null},{"id":"2306.00802","title":"Birth of a Transformer: A Memory Viewpoint","created":"2023-06-01","cited_by_count":161,"area":null},{"id":"2306.14892","title":"Supervised Pretraining Can Learn In-Context Reinforcement Learning","created":"2023-06-26","cited_by_count":139,"area":null},{"id":"2205.12685","title":"Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations","created":"2022-05-25","cited_by_count":116,"area":null},{"id":"2309.17249","title":"Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering","created":"2023-09-29","cited_by_count":94,"area":"lr"},{"id":"2403.15796","title":"Understanding Emergent Abilities of Language Models from the Loss Perspective","created":"2024-03-23","cited_by_count":94,"area":null},{"id":"2303.07971","title":"A Theory of Emergent In-Context Learning as Implicit Structure Induction","created":"2023-03-14","cited_by_count":86,"area":null},{"id":"2212.06713","title":"Structured Prompting: Scaling In-Context Learning to 1,000 Examples","created":"2022-12-13","cited_by_count":81,"area":null},{"id":"2212.09803","title":"Training Trajectories of Language Models Across Scales","created":"2022-12-19","cited_by_count":81,"area":null},{"id":"2306.06427","title":"Boosting Language Models Reasoning with Chain-of-Knowledge Prompting","created":"2023-06-10","cited_by_count":77,"area":"lr"},{"id":"2310.09881","title":"In-Context Learning with Iterative Demonstration Selection","created":"2023-10-15","cited_by_count":74,"area":null},{"id":"2302.00618","title":"Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models","created":"2023-02-01","cited_by_count":61,"area":"lr"},{"id":"2205.10183","title":"Prototypical Calibration for Few-shot Learning of Language Models","created":"2022-05-20","cited_by_count":58,"area":null},{"id":"2305.14328","title":"Benchmarking Machine Translation with Cultural Awareness","created":"2023-05-23","cited_by_count":53,"area":null},{"id":"2309.17382","title":"Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency","created":"2023-09-29","cited_by_count":46,"area":"wm"},{"id":"2206.05224","title":"A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction","created":"2022-06-10","cited_by_count":44,"area":null},{"id":"2304.04748","title":"Exploring Effective Factors for Improving Visual In-Context Learning","created":"2023-04-10","cited_by_count":43,"area":null},{"id":"2403.10854","title":"A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment","created":"2024-03-16","cited_by_count":43,"area":"mllm"},{"id":"2305.09137","title":"Pre-Training to Learn in Context","created":"2023-05-16","cited_by_count":42,"area":null},{"id":"2306.15091","title":"Understanding In-Context Learning via Supportive Pretraining Data","created":"2023-06-26","cited_by_count":41,"area":null},{"id":"2212.10670","title":"In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models","created":"2022-12-20","cited_by_count":37,"area":null},{"id":"2310.20046","title":"Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection","created":"2023-10-30","cited_by_count":37,"area":null},{"id":"2405.02580","title":"PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation","created":"2024-05-04","cited_by_count":37,"area":"rag"},{"id":"2503.05788","title":"Emergent Abilities in Large Language Models: A Survey","created":"2025-02-28","cited_by_count":37,"area":null},{"id":"2311.00237","title":"The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis","created":"2023-11-01","cited_by_count":34,"area":null},{"id":"2406.11801","title":"Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations","created":"2024-06-17","cited_by_count":32,"area":"alignment"},{"id":"2410.02811","title":"SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs","created":"2024-09-22","cited_by_count":31,"area":null},{"id":"2310.10873","title":"IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models","created":"2023-10-16","cited_by_count":27,"area":null},{"id":"2402.12530","title":"Parallel Structures in Pre-training Data Yield In-Context Learning","created":"2024-02-19","cited_by_count":26,"area":null},{"id":"2409.00031","title":"Quality Assessment in the Era of Large Models: A Survey","created":"2024-08-17","cited_by_count":26,"area":null},{"id":"2502.11544","title":"Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis","created":"2025-02-17","cited_by_count":26,"area":"lr"},{"id":"2402.13991","title":"Analysing The Impact of Sequence Composition on Language Model Pre-Training","created":"2024-02-21","cited_by_count":25,"area":null},{"id":"2405.16325","title":"SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs","created":"2024-05-25","cited_by_count":20,"area":null},{"id":"2306.13840","title":"Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data","created":"2023-06-24","cited_by_count":19,"area":null},{"id":"2310.08540","title":"Do pretrained Transformers Learn In-Context by Gradient Descent?","created":"2023-10-12","cited_by_count":18,"area":null},{"id":"2305.17040","title":"A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks","created":"2023-05-26","cited_by_count":17,"area":null},{"id":"2412.04237","title":"VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction","created":"2024-12-05","cited_by_count":15,"area":null},{"id":"2307.01201","title":"Schema-learning and rebinding as mechanisms of in-context learning and emergence","created":"2023-06-16","cited_by_count":14,"area":null},{"id":"2310.17639","title":"In-Context Learning Dynamics with Random Binary Sequences","created":"2023-10-26","cited_by_count":13,"area":null},{"id":"2311.09782","title":"More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering","created":"2023-11-16","cited_by_count":13,"area":null},{"id":"2305.15695","title":"Asking Before Acting: Gather Information in Embodied Decision Making with Language Models","created":"2023-05-25","cited_by_count":12,"area":null},{"id":"2408.07291","title":"Evaluating LLM-based Personal Information Extraction and Countermeasures","created":"2024-08-14","cited_by_count":12,"area":"safety"},{"id":"2306.06212","title":"Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions","created":"2023-06-09","cited_by_count":11,"area":null},{"id":"2407.19998","title":"Do LLMs Really Adapt to Domains? An Ontology Learning Perspective","created":"2024-07-29","cited_by_count":11,"area":null},{"id":"2309.06054","title":"Breaking through the learning plateaus of in-context learning in Transformer","created":"2023-09-12","cited_by_count":8,"area":null},{"id":"2406.11007","title":"Threat Modelling and Risk Analysis for Large Language Model (LLM)-Powered Applications","created":"2024-06-16","cited_by_count":8,"area":"safety"},{"id":"2207.00982","title":"Linguistically inspired roadmap for building biologically reliable protein language models","created":"2022-07-03","cited_by_count":7,"area":null},{"id":"2306.06800","title":"AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing","created":"2023-06-11","cited_by_count":7,"area":null},{"id":"2504.21263","title":"Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning","created":"2025-04-30","cited_by_count":7,"area":null},{"id":"2208.01009","title":"Few-shot Adaptation Works with UnpredicTable Data","created":"2022-08-01","cited_by_count":6,"area":null},{"id":"2310.06278","title":"BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models","created":"2023-10-10","cited_by_count":6,"area":null},{"id":"2401.14626","title":"Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2410.12543","title":"LLM-based Translation Inference with Iterative Bilingual Understanding","created":"2024-10-16","cited_by_count":6,"area":null},{"id":"2212.08686","title":"Evaluating Step-by-Step Reasoning through Symbolic Verification","created":"2022-12-16","cited_by_count":5,"area":"lr"},{"id":"2412.05710","title":"PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks","created":"2024-12-07","cited_by_count":5,"area":null},{"id":"2509.17552","title":"Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning","created":"2025-09-22","cited_by_count":5,"area":"lr"},{"id":"2601.20796","title":"Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers","created":"2026-01-28","cited_by_count":5,"area":"mllm"},{"id":"2307.07742","title":"SINC: Self-Supervised In-Context Learning for Vision-Language Tasks","created":"2023-07-15","cited_by_count":4,"area":null},{"id":"2504.19395","title":"ICL CIPHERS: Quantifying \"Learning\" in In-Context Learning via Substitution Ciphers","created":"2025-04-28","cited_by_count":4,"area":null},{"id":"2410.14049","title":"Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2504.10421","title":"Can We Edit LLMs for Long-Tail Biomedical Knowledge?","created":"2025-04-14","cited_by_count":3,"area":null},{"id":"2509.20882","title":"On Theoretical Interpretations of Concept-Based In-Context Learning","created":"2025-09-25","cited_by_count":3,"area":null},{"id":"2212.03760","title":"Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning","created":"2022-12-07","cited_by_count":2,"area":null},{"id":"2402.10424","title":"Pelican Soup Framework: A Theoretical Framework for Language Model Capabilities","created":"2024-02-16","cited_by_count":2,"area":null},{"id":"2405.11465","title":"Effective In-Context Example Selection through Data Compression","created":"2024-05-19","cited_by_count":2,"area":null},{"id":"2506.00507","title":"Exploring In-context Example Generation for Machine Translation","created":"2025-05-31","cited_by_count":2,"area":null},{"id":"2508.17391","title":"Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets","created":"2025-08-24","cited_by_count":2,"area":null},{"id":"2511.17506","title":"AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks","created":"2025-10-02","cited_by_count":2,"area":"lr"},{"id":"2603.18891","title":"PromptHub: Enhancing Multi-Prompt Visual In-Context Learning with Locality-Aware Fusion, Concentration and Alignment","created":"2026-03-19","cited_by_count":2,"area":null},{"id":"2403.19340","title":"Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models","created":"2024-03-28","cited_by_count":1,"area":null},{"id":"2603.21373","title":"PLR: Plackett-Luce for Reordering In-Context Learning Examples","created":"2026-03-22","cited_by_count":1,"area":null},{"id":"2302.06198","title":"Distinguishability Calibration to In-Context Learning","created":"2023-02-13","cited_by_count":0,"area":null},{"id":"2502.05233","title":"Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture","created":"2025-02-07","cited_by_count":0,"area":"rag"},{"id":"2505.14523","title":"Exploring Graph Representations of Logical Forms for Language Modeling","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2509.15676","title":"KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2608.24070","title":"Compression Trinity: Exploring Sparsity, Quantization, and Low-Rank Approximations for LLM Compression","created":"2026-08-25","cited_by_count":0,"area":null}],"next_cursor":null}