{"total":33,"rows":[{"id":"2403.03230","title":"Large language models surpass human experts in predicting neuroscience results","created":"2024-03-04","cited_by_count":69,"area":null},{"id":"2404.02418","title":"Auxiliary task demands mask the capabilities of smaller language models","created":"2024-04-03","cited_by_count":54,"area":null},{"id":"2402.01676","title":"Language models align with human judgments on key grammatical constructions","created":"2024-01-19","cited_by_count":49,"area":null},{"id":"2403.00025","title":"On the Challenges and Opportunities in Generative AI","created":"2024-02-28","cited_by_count":31,"area":null},{"id":"2503.07513","title":"Language Models Fail to Introspect About Their Knowledge of Language","created":"2025-03-10","cited_by_count":30,"area":null},{"id":"2502.20502","title":"On Benchmarking Human-Like Intelligence in Machines","created":"2025-02-27","cited_by_count":25,"area":null},{"id":"2309.06085","title":"BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models","created":"2023-09-12","cited_by_count":18,"area":null},{"id":"2510.16227","title":"What Can String Probability Tell Us About Grammaticality?","created":"2025-10-17","cited_by_count":16,"area":null},{"id":"2404.14883","title":"Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans Due to Impenetrable Semantic Reference","created":"2024-04-23","cited_by_count":15,"area":null},{"id":"2411.06096","title":"A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese","created":"2024-11-09","cited_by_count":13,"area":null},{"id":"2404.12957","title":"Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction","created":"2024-04-19","cited_by_count":7,"area":null},{"id":"2504.17075","title":"Agree to Disagree? A Meta-Evaluation of LLM Misgendering","created":"2025-04-23","cited_by_count":7,"area":null},{"id":"2506.21849","title":"The Consistency Hypothesis in Uncertainty Quantification for Large Language Models","created":"2025-06-27","cited_by_count":6,"area":null},{"id":"2306.07195","title":"Large language models and (non-)linguistic recursion","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2410.01487","title":"Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas","created":"2024-10-02","cited_by_count":5,"area":null},{"id":"2503.13390","title":"Aligned Probing: Relating Toxic Behavior and Model Internals","created":"2025-03-17","cited_by_count":5,"area":null},{"id":"2403.14578","title":"RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain","created":"2024-03-21","cited_by_count":4,"area":null},{"id":"2412.01330","title":"The \"LLM World of Words\" English free association norms generated by large language models","created":"2024-12-02","cited_by_count":4,"area":null},{"id":"2510.25356","title":"Prompting from the bench: Large-scale pretraining is not sufficient to prepare LLMs for ordinary meaning analysis","created":"2025-10-29","cited_by_count":4,"area":null},{"id":"2506.20666","title":"Cognitive models can reveal interpretable value trade-offs in language models","created":"2025-06-25","cited_by_count":3,"area":null},{"id":"2509.14405","title":"Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings","created":"2025-09-17","cited_by_count":3,"area":null},{"id":"2603.26539","title":"How Open Must Language Models be to Enable Reliable Scientific Inference?","created":"2026-03-27","cited_by_count":2,"area":null},{"id":"2504.05059","title":"MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2505.09005","title":"For GPT-4 as with Humans: Information Structure Predicts Acceptability of Long-Distance Dependencies","created":"2025-05-13","cited_by_count":1,"area":null},{"id":"2607.15175","title":"Linear representations of grammaticality in neural language models","created":"2026-07-16","cited_by_count":1,"area":null},{"id":"2608.03803","title":"M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2312.03749","title":"Conceptual Engineering Using Large Language Models","created":"2023-12-01","cited_by_count":0,"area":null},{"id":"2405.07495","title":"MacBehaviour: An R package for behavioural experimentation on large language models","created":"2024-05-13","cited_by_count":0,"area":null},{"id":"2504.16884","title":"Do Large Language Models know who did what to whom?","created":"2025-04-23","cited_by_count":0,"area":null},{"id":"2510.24934","title":"Disaggregation Reveals Hidden Training Dynamics: The Case of Agreement Attraction","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2512.12444","title":"Can GPT replace human raters? Validity and reliability of machine-generated norms for metaphors","created":"2025-12-13","cited_by_count":0,"area":null},{"id":"2603.12105","title":"To Words and Beyond: Probing Large Language Models for Sentence-Level Psycholinguistic Norms of Memorability and Reading Times","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2609.29328","title":"Grammatical \"grandmother neurons\" are rare in LLMs","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}