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Mario Giulianelli

ACL 2026×2ICML 2026×2评测与基准可解释性社会与人本智能体与工具对齐与偏好
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0 一作 · 4 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

4
作者位次 #5/7ACL 2026Poster· Natural Language Processing

Probing for Reading Times

Eleftheria Tsipidi · Samuel Kiegeland · Francesco Ignazio Re · Tianyang Xu · Mario Giulianelli · Karolina Stanczak · Ryan Cotterell

摘要

Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive signals about human processing. In this work, we probe language model representations for human reading times. Using regularized linear regression o...

可解释性对齐与偏好效率与系统OpenReview 项目页
作者位次 #2/3ACL 2026Poster· Natural Language Processing

Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue

Thomas P. Utting · Mario Giulianelli · Arabella Sinclair

摘要

We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost. We distinguish between goal-directed alternatives that realise a fixed communicative intent and goal-agnostic alternatives defined only by contextual pla...

评测与基准可解释性理论与学习动力学OpenReview 项目页

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