21 citations · 23 across the 14 of their papers we have counts for
5 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…
Learning to Reason Efficiently with A* Post-Training
Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov +3
Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language infer…
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…