21 citations · 23 across the 14 of their papers we have counts for
12 papers · 1 filter
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland +3
Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural language mo…
Prefix Parsing is Just Parsing
Clemente Pasti, Andreas Opedal, Timothy J. O'Donnell +2
Prefix parsing asks whether an input prefix can be extended to a complete string generated by a given grammar. In the weighted setting, it also provides prefix probabilities, which…
Rethinking Easy-to-Hard: Limits of Curriculum Learning in Post-Training for Deductive Reasoning
Maximilian Mordig, Andreas Opedal, Weiyang Liu +1
Curriculum learning (CL), motivated by the intuition that learning in increasing order of difficulty should ease generalization, is commonly adopted both in pre-training and post-t…
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3
Modeling student misconceptions in a realistic manner is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when ge…
Are Language Models Efficient Reasoners? A Perspective from Logic Programming
Andreas Opedal, Yanick Zengaffinen, Haruki Shirakami +5
Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: efficiency…
Generalized Measures of Anticipation and Responsivity in Online Language Processing
Mario Giulianelli, Andreas Opedal, Ryan Cotterell
We introduce a generalization of classic information-theoretic measures of predictive uncertainty in online language processing, based on the simulation of expected continuations o…
On the Role of Context in Reading Time Prediction
Andreas Opedal, Eleanor Chodroff, Ryan Cotterell +1
We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing eff…
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
Andreas Opedal, Alessandro Stolfo, Haruki Shirakami +5
There is increasing interest in employing large language models (LLMs) as cognitive models. For such purposes, it is central to understand which properties of human cognition are w…
An Exploration of Left-Corner Transformations
Andreas Opedal, Eleftheria Tsipidi, Tiago Pimentel +2
The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar pars…
Efficient Semiring-Weighted Earley Parsing
Andreas Opedal, Ran Zmigrod, Tim Vieira +2
This paper provides a reference description, in the form of a deduction system, of Earley's (1970) context-free parsing algorithm with various speed-ups. Our presentation includes…
World Models for Math Story Problems
Andreas Opedal, Niklas Stoehr, Abulhair Saparov +1
Solving math story problems is a complex task for students and NLP models alike, requiring them to understand the world as described in the story and reason over it to compute an a…
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang
Daphna Keidar, Andreas Opedal, Zhijing Jin +1
Languages are continuously undergoing changes, and the mechanisms that underlie these changes are still a matter of debate. In this work, we approach language evolution through the…