The study introduces a latent‑process multinomial processing tree (MPT) model to analyze human reading and comprehension of garden‑path sentences across four reading paradigms (eye tracking, uni‑ and bidirectional self‑paced reading, Maze). The model separates the likelihood of an incorrect initial analysis, the cost of encountering an incompatible continuation, and the cost of syntactic reanalysis, yielding more realistic parameter estimates when inattentive trials are considered. Cross‑validation shows that this MPT model predicts human reading patterns and end‑of‑trial judgments better than a model relying solely on large‑language‑model (LLM) surprisal, and that incorporating surprisal as an additional predictor further improves fit.
By Dario Paape, Tal Linzen, Shravan Vasishth
arXiv:2509.15114v2 Announce Type: replace
Abstract: A controversial test for Large Language Models concerns the ability to discern possible from impossible language. While some evidence attests to th...
By Evelina Leivada, Raquel Montero, Paolo Morosi, Natalia Moskvina, Tamara Serrano, Nikoleta Pantelidou, Marcel Aguilar, Fritz Guenther
The paper investigates how adjectival modifiers affect the semantic plausibility of events, using the Adept benchmark of 16,000 English sentence pairs that differ by a single adjective. Experiments show that sentence transformers, despite being conceptually suited to the task, underperform compared to models like RoBERTa. The authors provide an error analysis and discuss the implications of their findings for future work on balancing training and test data.
By Anna Golub, Beate Zywietz, Annerose Eichel
The study evaluates eleven autoregressive transformer models on English agreement attraction scenarios using a surprisal-based approach. Results show that while transformers match human reading times for prepositional phrase configurations, they perform poorly on object‑extracted relative clauses, with predictions diverging across models and failing to capture human interference patterns. The authors argue that current transformers cannot adequately model human morphosyntactic processing and call for more rigorous, comprehensive testing to avoid misleading conclusions from limited syntactic setups.
By Titus von der Malsburg, Sebastian Pad\'o
arXiv:2608. 10137v1 Announce Type: cross Abstract: Grammar Constrained Decoding (GCD) forces Language Models (LMs) to produce syntactically valid outputs by masking out non-conforming tokens at each step.
By I\c{s}{\i}l \"Ozg\"u, Yaoxuan Wu, Guy Van den Broeck, Miryung Kim
Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language model. I argue this claim is a tautology without further constraint: for any non-negative difficulty measure over units in context, there exists a language model whose surprisal is an affine function of it under mild technical conditions.
arXiv:2607.21574v2 Announce Type: replace
Abstract: Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some langu...
By Ryan Cotterell
As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming increasingly important for quantifying the uncertaint...
arXiv:2606. 25987v1 Announce Type: cross Abstract: Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language.
By Alexandre Bouayad
arXiv:2606. 07897v1 Announce Type: new Abstract: Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user.
By Alejandro Botas, Paul de Font-Reaulx, Luke Hewitt
The paper investigates energy-based transformers as predictors of reading difficulty, extending the use of transformer language models in psycholinguistics. It demonstrates that the energy measure from these models robustly predicts reading times across multiple corpora, outperforming traditional metrics like surprisal and attention entropy. In a controlled experiment on relative clause processing, energy captures known asymmetries, suggesting it may unify previously complementary predictors.
By Jakub Dotlacil, Ece Takmaz
arXiv:2602.13194v3 Announce Type: replace-cross
Abstract: Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong r...
By Weishun Zhong, Doron Sivan, Tankut Can, Mikhail Katkov, Misha Tsodyks