{"total":104,"rows":[{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576,"area":null},{"id":"2301.00234","title":"A Survey on In-context Learning","created":"2022-12-31","cited_by_count":721,"area":null},{"id":"2305.18654","title":"Faith and Fate: Limits of Transformers on Compositionality","created":"2023-05-29","cited_by_count":503,"area":null},{"id":"2302.03494","title":"A Categorical Archive of ChatGPT Failures","created":"2023-02-06","cited_by_count":168,"area":null},{"id":"2407.04622","title":"On scalable oversight with weak LLMs judging strong LLMs","created":"2024-07-05","cited_by_count":99,"area":null},{"id":"2406.05761","title":"The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models","created":"2024-06-09","cited_by_count":93,"area":null},{"id":"2303.11504","title":"Language Model Behavior: A Comprehensive Survey","created":"2023-03-20","cited_by_count":92,"area":null},{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86,"area":null},{"id":"2502.11221","title":"PlanGenLLMs: A Modern Survey of LLM Planning Capabilities","created":"2025-02-16","cited_by_count":83,"area":null},{"id":"2306.00946","title":"Exposing Attention Glitches with Flip-Flop Language Modeling","created":"2023-06-01","cited_by_count":75,"area":null},{"id":"2302.06100","title":"Can GPT-3 Perform Statutory Reasoning?","created":"2023-02-13","cited_by_count":73,"area":null},{"id":"2310.14735","title":"Unleashing the potential of prompt engineering for large language models","created":"2023-10-23","cited_by_count":72,"area":null},{"id":"2310.07064","title":"Large Language Models can Learn Rules","created":"2023-10-10","cited_by_count":69,"area":null},{"id":"2404.09939","title":"A Survey on Deep Learning for Theorem Proving","created":"2024-04-15","cited_by_count":57,"area":null},{"id":"2306.11489","title":"Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling","created":"2023-06-20","cited_by_count":56,"area":null},{"id":"2403.17104","title":"Attribute First, then Generate: Locally-attributable Grounded Text Generation","created":"2024-03-25","cited_by_count":54,"area":null},{"id":"2402.11917","title":"A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task","created":"2024-02-19","cited_by_count":49,"area":null},{"id":"2410.19878","title":"Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies","created":"2024-10-24","cited_by_count":47,"area":null},{"id":"2304.07810","title":"VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping","created":"2023-04-16","cited_by_count":46,"area":null},{"id":"2412.12472","title":"Knowledge Boundary of Large Language Models: A Survey","created":"2024-12-17","cited_by_count":40,"area":null},{"id":"2311.09278","title":"Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models","created":"2023-11-15","cited_by_count":39,"area":null},{"id":"2501.05435","title":"Neuro-Symbolic AI in 2024: A Systematic Review","created":"2025-01-09","cited_by_count":38,"area":null},{"id":"2410.15413","title":"A Comprehensive Evaluation of Cognitive Biases in LLMs","created":"2024-10-20","cited_by_count":34,"area":null},{"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":"2504.18838","title":"Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks","created":"2025-04-26","cited_by_count":28,"area":null},{"id":"2406.12158","title":"LLMs Are Prone to Fallacies in Causal Inference","created":"2024-06-18","cited_by_count":23,"area":null},{"id":"2311.07811","title":"In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax","created":"2023-11-13","cited_by_count":22,"area":null},{"id":"2311.12997","title":"Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks","created":"2023-11-21","cited_by_count":22,"area":null},{"id":"2401.12585","title":"SLANG: New Concept Comprehension of Large Language Models","created":"2024-01-23","cited_by_count":20,"area":null},{"id":"2307.11346","title":"CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study","created":"2023-07-21","cited_by_count":19,"area":null},{"id":"2401.18070","title":"Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?","created":"2024-01-31","cited_by_count":19,"area":null},{"id":"2405.17822","title":"Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action","created":"2024-05-28","cited_by_count":19,"area":null},{"id":"2412.04703","title":"Transformers Struggle to Learn to Search","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2501.01540","title":"BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery","created":"2025-01-02","cited_by_count":18,"area":null},{"id":"2401.17426","title":"Superiority of Multi-Head Attention in In-Context Linear Regression","created":"2024-01-30","cited_by_count":17,"area":null},{"id":"2405.05409","title":"Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing","created":"2024-05-08","cited_by_count":17,"area":null},{"id":"2408.03247","title":"Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons","created":"2024-08-06","cited_by_count":16,"area":null},{"id":"2402.02212","title":"A Survey to Recent Progress Towards Understanding In-Context Learning","created":"2024-02-03","cited_by_count":14,"area":null},{"id":"2504.01001","title":"Zero-shot Benchmarking: A Framework for Flexible and Scalable Automatic Evaluation of Language Models","created":"2025-04-01","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":"2407.19807","title":"Cool-Fusion: Fuse Large Language Models without Training","created":"2024-07-29","cited_by_count":12,"area":null},{"id":"2410.13502","title":"MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs","created":"2024-10-17","cited_by_count":12,"area":null},{"id":"2306.04347","title":"World Models for Math Story Problems","created":"2023-06-07","cited_by_count":11,"area":null},{"id":"2309.10371","title":"Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs","created":"2023-09-19","cited_by_count":11,"area":null},{"id":"2404.00560","title":"A Theory for Length Generalization in Learning to Reason","created":"2024-03-31","cited_by_count":11,"area":null},{"id":"2312.10321","title":"LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?","created":"2023-12-16","cited_by_count":10,"area":null},{"id":"2410.23299","title":"FVEval: Understanding Language Model Capabilities in Formal Verification of Digital Hardware","created":"2024-10-15","cited_by_count":10,"area":null},{"id":"2602.22441","title":"How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?","created":"2026-02-25","cited_by_count":10,"area":null},{"id":"2312.14856","title":"Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code","created":"2023-12-22","cited_by_count":9,"area":null},{"id":"2402.11532","title":"Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models","created":"2024-02-18","cited_by_count":9,"area":null},{"id":"2409.01806","title":"LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning","created":"2024-09-03","cited_by_count":9,"area":null},{"id":"2410.11672","title":"Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2311.08704","title":"Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains","created":"2023-11-15","cited_by_count":7,"area":null},{"id":"2402.06287","title":"AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems","created":"2024-02-09","cited_by_count":7,"area":null},{"id":"2403.05468","title":"Will GPT-4 Run DOOM?","created":"2024-03-08","cited_by_count":6,"area":null},{"id":"2502.19295","title":"Complex LLM Planning via Automated Heuristics Discovery","created":"2025-02-26","cited_by_count":6,"area":null},{"id":"2506.09433","title":"Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training","created":"2025-06-11","cited_by_count":5,"area":null},{"id":"2606.25178","title":"Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR","created":"2026-06-23","cited_by_count":5,"area":null},{"id":"2403.16097","title":"Can Language Models Pretend Solvers? Logic Code Simulation with LLMs","created":"2024-03-24","cited_by_count":4,"area":null},{"id":"2407.04067","title":"Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM","created":"2024-07-04","cited_by_count":4,"area":null},{"id":"2408.09150","title":"CogLM: Tracking Cognitive Development of Large Language Models","created":"2024-08-17","cited_by_count":4,"area":null},{"id":"2307.10432","title":"PharmacyGPT: The AI Pharmacist","created":"2023-07-19","cited_by_count":3,"area":null},{"id":"2403.18327","title":"$\\forall$uto$\\exists$val: Autonomous Assessment of LLMs in Formal Synthesis and Interpretation Tasks","created":"2024-03-27","cited_by_count":3,"area":null},{"id":"2502.09589","title":"Logical forms complement probability in understanding language model (and human) performance","created":"2025-02-13","cited_by_count":3,"area":null},{"id":"2503.22989","title":"FindTheFlaws: Annotated Errors for Detecting Flawed Reasoning and Scalable Oversight Research","created":"2025-03-29","cited_by_count":3,"area":null},{"id":"2511.06427","title":"Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop","created":"2025-11-09","cited_by_count":3,"area":null},{"id":"2606.17667","title":"Handling Feature Heterogeneity with Learnable Graph Patches","created":"2026-06-16","cited_by_count":3,"area":null},{"id":"2304.01333","title":"Classification of integers based on residue classes via modern deep learning algorithms","created":"2023-04-03","cited_by_count":2,"area":null},{"id":"2502.13953","title":"Benchmarking graph construction by large language models for coherence-driven inference","created":"2025-02-19","cited_by_count":2,"area":null},{"id":"2508.12754","title":"Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants","created":"2025-08-18","cited_by_count":2,"area":null},{"id":"2509.03540","title":"Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction","created":"2025-08-31","cited_by_count":2,"area":null},{"id":"2509.10414","title":"Is In-Context Learning Learning?","created":"2025-09-12","cited_by_count":2,"area":null},{"id":"2605.30590","title":"Counterfactual Evaluation Reveals Hidden Capability Profiles in Clinical LLMs and Agents","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2308.04161","title":"Current and Future Challenges in Knowledge Representation and Reasoning","created":"2023-08-08","cited_by_count":1,"area":null},{"id":"2312.17353","title":"Towards Auto-Modeling of Formal Verification for NextG Protocols: A Multimodal cross- and self-attention Large Language Model Approach","created":"2023-12-28","cited_by_count":1,"area":null},{"id":"2410.11265","title":"In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2412.14684","title":"Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines","created":"2024-12-19","cited_by_count":1,"area":null},{"id":"2501.06286","title":"Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks","created":"2025-01-10","cited_by_count":1,"area":null},{"id":"2502.19973","title":"Can Large Language Models Unveil the Mysteries? An Exploration of Their Ability to Unlock Information in Complex Scenarios","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2503.04848","title":"Three tiers of computation in transformers and in brain architectures","created":"2025-03-05","cited_by_count":1,"area":null},{"id":"2505.04736","title":"The promise and limits of LLMs in constructing proofs and hints for logic problems in intelligent tutoring systems","created":"2025-05-07","cited_by_count":1,"area":null},{"id":"2507.21257","title":"CompoST: A Benchmark for Analyzing the Ability of LLMs To Compositionally Interpret Questions in a QALD Setting","created":"2025-07-28","cited_by_count":1,"area":null},{"id":"2508.01763","title":"Reasoning Systems as Structured Processes: Foundations, Failures, and Formal Criteria","created":"2025-08-03","cited_by_count":1,"area":null},{"id":"2512.05765","title":"AGI Requires a Coordination Layer on Top of Pattern Repositories","created":"2025-12-05","cited_by_count":1,"area":null},{"id":"2512.20162","title":"Concept Generalization in Humans and Large Language Models: Insights from the Number Game","created":"2025-12-23","cited_by_count":1,"area":null},{"id":"2604.05396","title":"Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2310.12300","title":"Measuring Pointwise $\\mathcal{V}$-Usable Information In-Context-ly","created":"2023-10-18","cited_by_count":0,"area":null},{"id":"2311.08941","title":"Transformers in the Service of Description Logic-based Contexts","created":"2023-11-15","cited_by_count":0,"area":null},{"id":"2505.08389","title":"Towards Contamination Resistant Benchmarks","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2507.23167","title":"LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2509.10972","title":"Enhancing Computational Cognitive Architectures with LLMs: A Case Study","created":"2025-09-13","cited_by_count":0,"area":null},{"id":"2510.11812","title":"PHANTOM RECALL: When Familiar Puzzles Fool Smart Models","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2511.00476","title":"Remembering Unequally: Global and Disciplinary Bias in LLM Reconstruction of Scholarly Coauthor Lists","created":"2025-11-01","cited_by_count":0,"area":null},{"id":"2511.20194","title":"In-Context Compositional Learning via Sparse Coding Transformer","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2601.19605","title":"Decompose-and-Formalise: Recursively Verifiable Natural Language Inference","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2602.01132","title":"Don't Judge a Book by its Cover: Testing LLMs' Robustness Under Logical Obfuscation","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2603.21736","title":"The Reasoning Error About Reasoning: Why Different Types of Reasoning Require Different Representational Structures","created":"2026-03-23","cited_by_count":0,"area":null},{"id":"2606.06252","title":"Closing the Loop on Latent Reasoning via Test-Time Reconstruction","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2606.09449","title":"Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2607.08317","title":"Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models","created":"2026-07-09","cited_by_count":0,"area":null}],"next_cursor":"100"}