arXiv AI September 2, 2026 By Jakub Dotlacil, Ece Takmaz

Energy-Based Transformers as Predictors of Reading Difficulty

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.

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llms

arXiv Computation and Language

Aug 31

Diverging Transformer Predictions for Human Sentence Processing: A Comprehensive Analysis of Agreement Attraction Effects

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

llms benchmarks

arXiv AI

Jul 21

Probing the Difficulty Perception Mechanism of Large Language Models

arXiv:2510. 05969v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed on complex reasoning tasks, yet little is known about their ability to internally evaluate problem difficulty, which is an essential capability for adaptive reasoning and efficient resource allocation.

By Sunbowen Lee, Qingyu Yin, Chak Tou Leong, Jialiang Zhang, Yicheng Gong, Shiwen Ni, Min Yang, Xiaoyu Shen

llms benchmarks

arXiv Computation and Language

Sep 1

Vectors from Larger Language Models Predict Human Reading Time and fMRI Data More Poorly when Dimensionality Expansion is Controlled

arXiv:2505.12196v2 Announce Type: replace Abstract: The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conject...

By Yi-Chien Lin, Hongao Zhu, William Schuler

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arXiv Machine Learning

Aug 6

Kathleen Writes: Autoregressive Generation and Data Scaling Without Attention

arXiv:2608. 04678v1 Announce Type: cross Abstract: Papers 1-2 of the Kathleen series showed that a byte-level, attention-free architecture built from a wavetable encoder and multi-scale reverberant state can match strong baselines on classification at ~450-700K parameters, without pretraining.

By George Fountzoulas

llms rag nlp benchmarks

arXiv AI

Jul 10

LEXIC: Lightweight Eye-tracking eXtension via Injected Complexity

arXiv:2607. 08152v1 Announce Type: cross Abstract: On the recent EyeBench benchmark, predicting reading comprehension from eye movements exposes a stark gap: text-aware models using pretrained language models reach 56--63% AUROC, while gaze-only models operate at chance.

By Sumin Lee, Kyeonghun Kim, Subeen Lee, Jiwon Yang, Tien Nguyen, Ken Ying-Kai Liao, Nam-Joon Kim

llms benchmarks

arXiv AI

2d ago

Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling

arXiv:2609.08981v1 Announce Type: cross Abstract: A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing...

By Arman Adibi, Alireza Jafari, Mohammad Ghavamzadeh, Hadi Daneshmand

llms diffusion

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