Hugging Face Trending Papers
Jul 23

Surprisal Theory is Tautological (without Rational Grounding)

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 Computation and Language
2d ago

Large Language Model probabilities cannot reliably distinguish between possible and impossible language

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
arXiv Computation and Language
Sep 25

LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigms

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 Computation and Language
Sep 16

surprisal is Not a Theory

The article argues that Surprisal Theory, often presented as a computational-level explanation, is not a theory in its own right. It contends that using large language model (LLM) surprisals without considering the underlying representational and algorithmic choices obscures the theory’s commitments. The authors demonstrate through three analyses that algorithm and architecture significantly influence language model probabilities, urging researchers to reassess treating LLM surprisals as interchangeable.

By Andr\'es Bux\'o-Lugo, Aniello De Santo, Morgan Grobol, Ryan J. Hubbard, Cassandra L. Jacobs
arXiv Computation and Language
Oct 22

Why is AI-generated language so hard to understand?

The paper investigates why AI-generated text has become harder to understand, focusing on psycholinguistic features linked to human processing difficulty such as object relative clauses, long syntactic dependencies, and low-frequency n-grams. Using a new corpus of AI-generated text from diverse domains, the study finds that frontier and near-frontier language models employ these difficult features more frequently than paraphrases from round-trip translation and human writing, including more inanimate subjects with animate verbs, longer dependencies, stacked nouns, and reduced object relatives. The authors hypothesize that AI models prioritize information density for tasks like math, code, and reasoning, diverging from human communication goals, and demonstrate this by fine‑tuning a model with a length penalty, which reduces human‑like linguistic behavior.

By Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald