An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal

2026-04-20

AI Summary

This study addresses the limited ability of current neural language models to predict human reading difficulty in garden-path sentences using surprisal, which fails to account for the associated cognitive load. To bridge this gap, the authors propose fine-tuning off-the-shelf neural language models on human reading time data from garden-path constructions, using supervised optimization to align model surprisal more closely with empirical reading behavior. The experiments demonstrate, for the first time, that such fine-tuned models can simultaneously explain both garden-path effects and reading times in naturalistic contexts without compromising general language modeling performance. This approach significantly improves prediction accuracy across both types of data, offering a unified account of human sentence processing grounded in modern language models.

Technology Category

• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word given its context. Computational psycholinguistics has tested this hypothesis using language models (LMs) as proxies for human prediction. While surprisal derived from recent neural LMs generally captures human processing difficulty on naturalistic corpora that predominantly consist of simple sentences, it severely underestimates processing difficulty on sentences that require syntactic disambiguation (garden-path effects). This leads to the claim that the processing difficulty of such sentences cannot be reduced to surprisal, although it remains possible that neural LMs simply differ from humans in next-word prediction. In this paper, we investigate whether it is truly impossible to construct a neural LM that can explain garden-path effects via surprisal. Specifically, instead of evaluating off-the-shelf neural LMs, we fine-tune these LMs on garden-path sentences so as to better align surprisal-based reading-time estimates with actual human reading times. Our results show that fine-tuned LMs do not overfit and successfully capture human reading slowdowns on held-out garden-path items; they even improve predictive power for human reading times on naturalistic corpora and preserve their general LM capabilities. These results provide an existence proof for a neural LM that can explain both garden-path effects and naturalistic reading times via surprisal, but also raise a theoretical question: what kind of evidence can truly falsify surprisal theory?

Problem

• garden-path effects
• surprisal
• neural language models
• sentence processing difficulty
• syntactic disambiguation

Innovation

• surprisal
• garden-path effects
• neural language models
• fine-tuning
• human reading times