{"total":20,"rows":[{"id":"2212.12131","title":"Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?","created":"2022-12-23","cited_by_count":113,"area":null},{"id":"2405.15750","title":"Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidence","created":"2024-05-24","cited_by_count":35,"area":null},{"id":"2205.11463","title":"Context Limitations Make Neural Language Models More Human-Like","created":"2022-05-23","cited_by_count":34,"area":null},{"id":"2104.05433","title":"Multilingual Language Models Predict Human Reading Behavior","created":"2021-04-12","cited_by_count":29,"area":null},{"id":"2310.06408","title":"Humans and language models diverge when predicting repeating text","created":"2023-10-10","cited_by_count":13,"area":null},{"id":"2310.17774","title":"Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?","created":"2023-10-26","cited_by_count":13,"area":null},{"id":"2106.06849","title":"Can Transformer Language Models Predict Psychometric Properties?","created":"2021-06-12","cited_by_count":8,"area":null},{"id":"2404.19178","title":"Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics","created":"2024-04-30","cited_by_count":6,"area":null},{"id":"2205.06203","title":"Predicting Human Psychometric Properties Using Computational Language Models","created":"2022-05-12","cited_by_count":5,"area":null},{"id":"2402.05455","title":"Large Language Models for Psycholinguistic Plausibility Pretesting","created":"2024-02-08","cited_by_count":5,"area":null},{"id":"2503.14728","title":"Strategic resource allocation in memory encoding: An efficiency principle shaping language processing","created":"2025-03-18","cited_by_count":2,"area":null},{"id":"2603.09872","title":"N-gram-like Language Models Predict Naturalistic Reading Time Best","created":"2026-03-10","cited_by_count":2,"area":null},{"id":"2210.01869","title":"Memory in humans and deep language models: Linking hypotheses for model augmentation","created":"2022-10-04","cited_by_count":1,"area":null},{"id":"2210.12958","title":"Composition, Attention, or Both?","created":"2022-10-24","cited_by_count":1,"area":null},{"id":"2506.16550","title":"A Free Probabilistic Framework for Analyzing the Transformer-based Language Models","created":"2025-06-19","cited_by_count":1,"area":null},{"id":"2601.09886","title":"Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal","created":"2026-01-14","cited_by_count":1,"area":null},{"id":"2602.04570","title":"Can LLMs capture stable human-generated sentence entropy measures?","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2503.20088","title":"Generative Linguistics, Large Language Models, and the Social Nature of Scientific Success","created":"2025-03-25","cited_by_count":0,"area":null},{"id":"2603.12050","title":"Translationese as a Rational Response to Translation Task Difficulty","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2607.16549","title":"Encoding EEG Signals to Examine Human-Like Next-Word Prediction Behaviour in Language Models","created":"2026-07-17","cited_by_count":0,"area":null}],"next_cursor":null}