{"total":37,"rows":[{"id":"2308.05374","title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","created":"2023-08-10","cited_by_count":401,"area":null},{"id":"2401.03065","title":"CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution","created":"2024-01-05","cited_by_count":300,"area":null},{"id":"2307.10169","title":"Challenges and Applications of Large Language Models","created":"2023-07-19","cited_by_count":254,"area":null},{"id":"2403.06963","title":"The pitfalls of next-token prediction","created":"2024-03-11","cited_by_count":160,"area":null},{"id":"2308.13067","title":"Causal Parrots: Large Language Models May Talk Causality But Are Not Causal","created":"2023-08-24","cited_by_count":150,"area":null},{"id":"2304.07438","title":"Tractable Control for Autoregressive Language Generation","created":"2023-04-15","cited_by_count":65,"area":null},{"id":"2210.00881","title":"Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network","created":"2022-09-23","cited_by_count":36,"area":null},{"id":"2409.13724","title":"Logically Consistent Language Models via Neuro-Symbolic Integration","created":"2024-09-09","cited_by_count":30,"area":null},{"id":"2504.15266","title":"Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction","created":"2025-04-21","cited_by_count":29,"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":"2412.04703","title":"Transformers Struggle to Learn to Search","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2209.15093","title":"Unpacking Large Language Models with Conceptual Consistency","created":"2022-09-29","cited_by_count":18,"area":null},{"id":"2305.03353","title":"MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic","created":"2023-05-05","cited_by_count":18,"area":null},{"id":"2401.12947","title":"Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion","created":"2024-01-23","cited_by_count":14,"area":null},{"id":"2211.00609","title":"A Simple, Yet Effective Approach to Finding Biases in Code Generation","created":"2022-10-31","cited_by_count":8,"area":null},{"id":"2306.10645","title":"Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix)","created":"2023-06-18","cited_by_count":6,"area":null},{"id":"2307.01540","title":"Learning to Prompt in the Classroom to Understand AI Limits: A pilot study","created":"2023-07-04","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":"2407.17396","title":"Systematic Relational Reasoning With Epistemic Graph Neural Networks","created":"2024-07-24","cited_by_count":4,"area":null},{"id":"2305.03445","title":"LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models","created":"2023-05-05","cited_by_count":3,"area":null},{"id":"2501.15857","title":"Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?","created":"2025-01-27","cited_by_count":3,"area":null},{"id":"2512.20907","title":"PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding","created":"2025-12-24","cited_by_count":3,"area":null},{"id":"2304.09276","title":"Towards a Neural Lambda Calculus: Neurosymbolic AI Applied to the Foundations of Functional Programming","created":"2023-04-18","cited_by_count":2,"area":null},{"id":"2311.02765","title":"Rule Learning as Machine Translation using the Atomic Knowledge Bank","created":"2023-11-05","cited_by_count":2,"area":null},{"id":"2407.04899","title":"Algorithmic Language Models with Neurally Compiled Libraries","created":"2024-07-06","cited_by_count":2,"area":null},{"id":"2412.14708","title":"Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey","created":"2024-12-19","cited_by_count":2,"area":null},{"id":"2508.05474","title":"Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?","created":"2025-08-07","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":"2210.02095","title":"ChemAlgebra: Algebraic Reasoning on Chemical Reactions","created":"2022-10-05","cited_by_count":1,"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":"2502.05416","title":"Deep Generative Models with Hard Linear Equality Constraints","created":"2025-02-08","cited_by_count":1,"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":"2410.04347","title":"Latent Feature Mining for Predictive Model Enhancement with Large Language Models","created":"2024-10-06","cited_by_count":0,"area":null},{"id":"2502.08415","title":"FSLI: An Interpretable Formal Semantic System for One-Dimensional Ordering Inference","created":"2025-02-12","cited_by_count":0,"area":null},{"id":"2509.04470","title":"COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2602.07602","title":"Object-Oriented Transition Modeling with Inductive Logic Programming","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2608.16707","title":"Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors","created":"2026-08-17","cited_by_count":0,"area":null}],"next_cursor":null}