{"total":111,"rows":[{"id":"2210.03629","title":"ReAct: Synergizing Reasoning and Acting in Language Models","created":"2022-10-06","cited_by_count":8695,"area":"lr"},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576,"area":null},{"id":"2210.02414","title":"GLM-130B: An Open Bilingual Pre-trained Model","created":"2022-10-05","cited_by_count":843,"area":null},{"id":"2302.00093","title":"Large Language Models Can Be Easily Distracted by Irrelevant Context","created":"2023-01-31","cited_by_count":841,"area":null},{"id":"2310.03714","title":"DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines","created":"2023-10-05","cited_by_count":832,"area":"synth"},{"id":"2302.00923","title":"Multimodal Chain-of-Thought Reasoning in Language Models","created":"2023-02-02","cited_by_count":752,"area":"lr"},{"id":"2307.13702","title":"Measuring Faithfulness in Chain-of-Thought Reasoning","created":"2023-07-17","cited_by_count":703,"area":"lr"},{"id":"2210.03493","title":"Automatic Chain of Thought Prompting in Large Language Models","created":"2022-10-07","cited_by_count":688,"area":"lr"},{"id":"2210.11610","title":"Large Language Models Can Self-Improve","created":"2022-10-20","cited_by_count":664,"area":"lr"},{"id":"2211.10435","title":"PAL: Program-aided Language Models","created":"2022-11-18","cited_by_count":652,"area":"lr"},{"id":"2307.06435","title":"A Comprehensive Overview of Large Language Models","created":"2023-07-12","cited_by_count":648,"area":null},{"id":"2210.02406","title":"Decomposed Prompting: A Modular Approach for Solving Complex Tasks","created":"2022-10-05","cited_by_count":558,"area":null},{"id":"2210.03057","title":"Language Models are Multilingual Chain-of-Thought Reasoners","created":"2022-10-06","cited_by_count":530,"area":"lr"},{"id":"2302.06476","title":"Is ChatGPT a General-Purpose Natural Language Processing Task Solver?","created":"2023-02-08","cited_by_count":488,"area":null},{"id":"2210.00720","title":"Complexity-Based Prompting for Multi-Step Reasoning","created":"2022-10-03","cited_by_count":388,"area":"lr"},{"id":"2212.10071","title":"Large Language Models Are Reasoning Teachers","created":"2022-12-20","cited_by_count":329,"area":"lr"},{"id":"2212.14024","title":"Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP","created":"2022-12-28","cited_by_count":318,"area":"rag"},{"id":"2504.11456","title":"DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning","created":"2025-04-15","cited_by_count":280,"area":"lr"},{"id":"2212.09597","title":"Reasoning with Language Model Prompting: A Survey","created":"2022-12-19","cited_by_count":252,"area":"lr"},{"id":"2304.09797","title":"Progressive-Hint Prompting Improves Reasoning in Large Language Models","created":"2023-04-19","cited_by_count":147,"area":"lr"},{"id":"2305.18703","title":"Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey","created":"2023-05-30","cited_by_count":135,"area":null},{"id":"2209.07686","title":"Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango","created":"2022-09-16","cited_by_count":128,"area":"lr"},{"id":"2402.03578","title":"LLM Multi-Agent Systems: Challenges and Open Problems","created":"2024-02-05","cited_by_count":120,"area":"agents"},{"id":"2302.12822","title":"Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data","created":"2023-02-24","cited_by_count":119,"area":"lr"},{"id":"2305.11860","title":"Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs","created":"2023-05-19","cited_by_count":115,"area":"lr"},{"id":"2211.09066","title":"Teaching Algorithmic Reasoning via In-context Learning","created":"2022-11-15","cited_by_count":112,"area":"lr"},{"id":"2306.06427","title":"Boosting Language Models Reasoning with Chain-of-Knowledge Prompting","created":"2023-06-10","cited_by_count":77,"area":"lr"},{"id":"2309.15129","title":"Evaluating Cognitive Maps and Planning in Large Language Models with CogEval","created":"2023-09-25","cited_by_count":77,"area":null},{"id":"2310.09881","title":"In-Context Learning with Iterative Demonstration Selection","created":"2023-10-15","cited_by_count":74,"area":null},{"id":"2311.11797","title":"Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents","created":"2023-11-20","cited_by_count":72,"area":"lr"},{"id":"2211.13892","title":"Complementary Explanations for Effective In-Context Learning","created":"2022-11-25","cited_by_count":71,"area":null},{"id":"2310.00280","title":"Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration","created":"2023-09-30","cited_by_count":70,"area":"lr"},{"id":"2305.02317","title":"Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings","created":"2023-05-03","cited_by_count":69,"area":"lr"},{"id":"2209.08655","title":"Enabling Conversational Interaction with Mobile UI using Large Language Models","created":"2022-09-18","cited_by_count":66,"area":null},{"id":"2210.01296","title":"Recitation-Augmented Language Models","created":"2022-10-04","cited_by_count":65,"area":null},{"id":"2310.04959","title":"Towards Better Chain-of-Thought Prompting Strategies: A Survey","created":"2023-10-08","cited_by_count":62,"area":"lr"},{"id":"2305.03453","title":"T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering","created":"2023-05-05","cited_by_count":60,"area":"lr"},{"id":"2310.15166","title":"Large Language Models are Visual Reasoning Coordinators","created":"2023-10-23","cited_by_count":58,"area":"lr"},{"id":"2305.12627","title":"MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction","created":"2023-05-22","cited_by_count":50,"area":null},{"id":"2305.09993","title":"Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling","created":"2023-05-17","cited_by_count":47,"area":"lr"},{"id":"2402.05403","title":"In-Context Principle Learning from Mistakes","created":"2024-02-08","cited_by_count":44,"area":null},{"id":"2407.06089","title":"Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models","created":"2024-07-08","cited_by_count":44,"area":null},{"id":"2305.13252","title":"\"According to ...\": Prompting Language Models Improves Quoting from Pre-Training Data","created":"2023-05-22","cited_by_count":42,"area":null},{"id":"2210.12217","title":"Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning","created":"2022-10-21","cited_by_count":38,"area":null},{"id":"2305.05181","title":"MoT: Memory-of-Thought Enables ChatGPT to Self-Improve","created":"2023-05-09","cited_by_count":38,"area":"lr"},{"id":"2304.05970","title":"Boosted Prompt Ensembles for Large Language Models","created":"2023-04-12","cited_by_count":36,"area":"lr"},{"id":"2402.02389","title":"KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion","created":"2024-02-04","cited_by_count":36,"area":null},{"id":"2305.14333","title":"Automatic Model Selection with Large Language Models for Reasoning","created":"2023-05-23","cited_by_count":35,"area":"lr"},{"id":"2308.00304","title":"Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models","created":"2023-08-01","cited_by_count":33,"area":null},{"id":"2410.05459","title":"From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency","created":"2024-10-07","cited_by_count":29,"area":"lr"},{"id":"2312.11336","title":"DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation","created":"2023-12-18","cited_by_count":27,"area":"lr"},{"id":"2210.01293","title":"ThinkSum: Probabilistic reasoning over sets using large language models","created":"2022-10-04","cited_by_count":23,"area":"lr"},{"id":"2307.01972","title":"Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification","created":"2023-07-05","cited_by_count":19,"area":null},{"id":"2310.01444","title":"Adapting LLM Agents with Universal Feedback in Communication","created":"2023-10-01","cited_by_count":19,"area":"agents"},{"id":"2212.08286","title":"ALERT: Adapting Language Models to Reasoning Tasks","created":"2022-12-16","cited_by_count":17,"area":"lr"},{"id":"2303.14310","title":"GPT is becoming a Turing machine: Here are some ways to program it","created":"2023-03-25","cited_by_count":16,"area":null},{"id":"2310.11430","title":"An Empirical Study of Translation Hypothesis Ensembling with Large Language Models","created":"2023-10-17","cited_by_count":16,"area":null},{"id":"2506.06485","title":"Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict","created":"2025-06-06","cited_by_count":16,"area":null},{"id":"2212.06295","title":"Despite \"super-human\" performance, current LLMs are unsuited for decisions about ethics and safety","created":"2022-12-13","cited_by_count":13,"area":null},{"id":"2404.14812","title":"Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns","created":"2024-04-23","cited_by_count":13,"area":"lr"},{"id":"2503.17363","title":"Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique","created":"2025-03-21","cited_by_count":13,"area":"lr"},{"id":"2407.11422","title":"Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models","created":"2024-07-16","cited_by_count":12,"area":"mllm"},{"id":"2408.05357","title":"SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions","created":"2024-08-09","cited_by_count":12,"area":null},{"id":"2408.09849","title":"Importance Weighting Can Help Large Language Models Self-Improve","created":"2024-08-19","cited_by_count":12,"area":"synth"},{"id":"2310.06692","title":"Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models","created":"2023-10-10","cited_by_count":11,"area":"lr"},{"id":"2411.17792","title":"H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs","created":"2024-11-26","cited_by_count":11,"area":"alignment"},{"id":"2306.07536","title":"TART: A plug-and-play Transformer module for task-agnostic reasoning","created":"2023-06-13","cited_by_count":10,"area":"lr"},{"id":"2306.08042","title":"FLamE: Few-shot Learning from Natural Language Explanations","created":"2023-06-13","cited_by_count":10,"area":null},{"id":"2312.15395","title":"Prompt Valuation Based on Shapley Values","created":"2023-12-24","cited_by_count":10,"area":null},{"id":"2410.09220","title":"M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought","created":"2024-10-11","cited_by_count":9,"area":"lr"},{"id":"2410.10074","title":"Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning","created":"2024-10-14","cited_by_count":9,"area":null},{"id":"2308.08780","title":"Exploring Demonstration Ensembling for In-context Learning","created":"2023-08-17","cited_by_count":8,"area":null},{"id":"2311.06555","title":"LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction","created":"2023-11-11","cited_by_count":8,"area":null},{"id":"2404.12728","title":"Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?","created":"2024-04-19","cited_by_count":8,"area":"lr"},{"id":"2305.17727","title":"Learning a Structural Causal Model for Intuition Reasoning in Conversation","created":"2023-05-28","cited_by_count":7,"area":"lr"},{"id":"2402.04975","title":"ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12","created":"2024-02-07","cited_by_count":7,"area":null},{"id":"2312.04087","title":"VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models","created":"2023-12-07","cited_by_count":6,"area":"mllm"},{"id":"2503.02670","title":"Multidimensional Consistency Improves Reasoning in Language Models","created":"2025-03-04","cited_by_count":6,"area":"lr"},{"id":"2212.09667","title":"Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI","created":"2022-12-19","cited_by_count":5,"area":null},{"id":"2308.04041","title":"InfeRE: Step-by-Step Regex Generation via Chain of Inference","created":"2023-08-08","cited_by_count":5,"area":null},{"id":"2309.12940","title":"Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models","created":"2023-09-22","cited_by_count":5,"area":null},{"id":"2402.17226","title":"Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models","created":"2024-02-27","cited_by_count":5,"area":"lr"},{"id":"2406.10249","title":"A Reality check of the benefits of LLM in business","created":"2024-06-09","cited_by_count":5,"area":null},{"id":"2503.12855","title":"VITED: Video Temporal Evidence Distillation","created":"2025-03-17","cited_by_count":5,"area":null},{"id":"2311.07099","title":"Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning","created":"2023-11-13","cited_by_count":4,"area":null},{"id":"2401.11725","title":"Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models","created":"2024-01-22","cited_by_count":4,"area":null},{"id":"2502.08972","title":"Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning","created":"2025-02-13","cited_by_count":4,"area":null},{"id":"2502.15132","title":"CoT-ICL Lab: A Synthetic Framework for Studying Chain-of-Thought Learning from In-Context Demonstrations","created":"2025-02-21","cited_by_count":4,"area":"lr"},{"id":"2604.06794","title":"GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering","created":"2026-04-08","cited_by_count":4,"area":"lr"},{"id":"2310.18358","title":"A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models","created":"2023-10-24","cited_by_count":3,"area":null},{"id":"2501.12226","title":"CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning","created":"2025-01-21","cited_by_count":3,"area":"lr"},{"id":"2305.13267","title":"Enhance Reasoning Ability of Visual-Language Models via Large Language Models","created":"2023-05-22","cited_by_count":2,"area":"lr"},{"id":"2308.14536","title":"Spoken Language Intelligence of Large Language Models for Language Learning","created":"2023-08-28","cited_by_count":2,"area":null},{"id":"2311.09558","title":"What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception","created":"2023-11-16","cited_by_count":2,"area":null},{"id":"2402.15764","title":"Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models","created":"2024-02-24","cited_by_count":2,"area":"lr"},{"id":"2501.05662","title":"Cascaded Self-Evaluation Augmented Training for Lightweight Multimodal LLMs","created":"2025-01-10","cited_by_count":2,"area":"lr"},{"id":"2502.04492","title":"Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents","created":"2025-02-06","cited_by_count":2,"area":"agents"},{"id":"2502.12304","title":"Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2604.25299","title":"The Thinking Pixel: Recursive Sparse Reasoning in Multimodal Diffusion Latents","created":"2026-04-28","cited_by_count":2,"area":"lr"},{"id":"2605.05802","title":"Selective Rollout: Mid-Trajectory Termination for Multi-Sample Agent RL","created":"2026-05-07","cited_by_count":2,"area":"lr"}],"next_cursor":"100"}