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cs.CL2026

Post-Training Language Models for Crosslingual Consistency

Tianyu Liu, Jirui Qi, Mrinmaya Sachan +3

Language models often respond inconsistently to translation-equivalent prompts across languages, undermining the reliability of multilingual systems. To quantify this, we give an i…

cs.CL2026

On the Proper Treatment of Units in Surprisal Theory

Samuel Kiegeland, Vésteinn Snæbjarnarson, Tim Vieira +1

Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a unit underspecified. In practic…

cs.CL2026

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…

cs.CL2026

Ensembling Language Models with Sequential Monte Carlo

Robin Shing Moon Chan, Tianyu Liu, Samuel Kiegeland +5

Practitioners have access to an abundance of language models and prompting strategies for solving many language modeling tasks; yet prior work shows that modeling performance is hi…

cs.CL2026

Transducing Language Models

Vésteinn Snæbjarnarson, Samuel Kiegeland, Tianyu Liu +3

Modern language models define distributions over strings, but downstream tasks often require different output formats. For instance, a model that generates byte-pair strings does n…

cs.CL2026

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…