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Hal Daumé Iii

ACL 2026×3CHI 2026×3社会与人本多模态评测与基准对齐与偏好强化学习与后训练
新星分资深
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0 一作 · 6 篇 · ACL 2026 / CHI 2026

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2026 顶会论文

6
作者位次 #3/3ACL 2026Poster· Natural Language Processing

SMARTER : A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models

Huy Nghiem · Advik Sachdeva · Hal Daumé Iii

摘要

To address toxic content on social media, we introduce SMARTER, a data-efficient 2-stage framework for explainable content moderation using Large Language Models (LLMs). In Stage 1, we leverage LLMs’ own outputs to generate synthetic explanations for correct and incorrect labels, enabling prefere...

对齐与偏好多模态强化学习与后训练OpenReview 项目页
作者位次 #6/6ACL 2026Poster· Natural Language Processing

Can You Make It Sound Like You? Post-Editing LLM -Generated Text for Personal Style

Connor Baumler · Calvin Bao · Huy Nghiem · Xinchen Yang · Marine Carpuat · Hal Daumé Iii

摘要

Despite the growing use of large language models (LLMs) for writing tasks, users may hesitate to rely on LLMs when personal style is important. Post-editing LLM-generated drafts or translations is a common collaborative writing strategy, but it remains unclear whether users can effectively reshap...

评测与基准多语言与语言学社会与人本OpenReview 项目页
作者位次 #4/6ACL 2026Findings· Findings of ACL

Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms

Dayeon Ki · Yu Hou · Rachel Rudinger · Hal Daumé Iii · Marine Carpuat · Fumeng Yang

摘要

Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication with native speakers (NS). NNS increasingly make use of Artificial Intelligence (AI) tools to learn these words. We stu...

评测与基准社会与人本可解释性OpenReview 项目页
作者位次 #2/2CHI 2026Oral· Human-Computer Interaction

When Stereotypes GTG: The Impact of Predictive Text Suggestions on Gender Bias in Human-AI Co-Writing

Connor Baumler · Hal Daumé Iii

社会与人本OpenReview

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