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Samuel Kiegeland

ACL 2026×2ICLR 2026对齐与偏好多模态可解释性效率与系统数据与预训练
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1 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

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作者位次 #2/7ACL 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
一作ACL 2026Poster· Natural Language Processing

On the Proper Treatment of Units in Surprisal Theory

Samuel Kiegeland · Vésteinn Snæbjarnarson · Tim Vieira · Ryan Cotterell

摘要

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 practice, experimental stimuli are segmented into linguistically motivated units (e.g., words), while pretrained language mo...

数据与预训练推理对齐与偏好OpenReview 项目页

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