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Atrey Desai

ACL 2026×3检索与知识社会与人本评测与基准推理可解释性
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一作贡献30
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1 一作 · 3 篇 · ACL 2026

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2026 顶会论文

3
作者位次 #5/10ACL 2026Poster· Natural Language Processing

B ench M arker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks

Nishant Balepur · Bhavya Rajasekaran · Hyunjin Jane Oh · Michael Xie · Atrey Desai · Vipul Gupta · Steven James Moore · Eunsol Choi · Rachel Rudinger · Jordan Lee Boyd-Graber

摘要

Multiple-choice question answering (MCQA) is standard in NLP, but benchmarks lack rigorous quality control. We present BenchMarker, an education-inspired toolkit using LLM judges to flag three common MCQ flaws: 1) contamination—items appearing exactly online; 2) shortcuts—cues in the choices that...

评测与基准检索与知识社会与人本OpenReview 项目页
作者位次 #2/3ACL 2026Poster· Natural Language Processing

Test-Time Reasoners Are Strategic Multiple-Choice Test-Takers

Nishant Balepur · Atrey Desai · Rachel Rudinger

摘要

Large language models (LLMs) now give reasoning before answering, excelling in tasks like multiple-choice question answering (MCQA). Yet, a concern is that LLMs do not solve MCQs as intended, as work finds LLMs sans reasoning succeed in MCQA without using the question, i.e., choices-only. Such pa...

推理检索与知识OpenReview 项目页
一作ACL 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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