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Sathvik Nair

ACL 2026×2社会与人本推理可解释性对齐与偏好
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一作贡献30
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方向广度8
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1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal

Sathvik Nair · Byung-Doh Oh

摘要

How predictable a word is can be generally quantified in two ways: using human responses to the cloze task or using probabilities from language models (LMs). When used as predictors of processing effort, LM probabilities outperform probabilities derived from cloze data. However, it is important t...

推理社会与人本OpenReview 项目页
作者位次 #2/2ACL 2026Findings· Findings of ACL

Filling in the Mechanisms: How do LM s Learn Filler-Gap Dependencies under Developmental Constraints?

Atrey Desai · Sathvik Nair

摘要

For humans, filler-gap dependencies require a shared representation across different syntactic constructions. Although causal analyses suggest this may also be true for LLMs (Boguraev et al., 2025), it is still unclear if such a representation also exists for language models trained on developmen...

可解释性社会与人本对齐与偏好OpenReview 项目页

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