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Ziwei Zhu

ACL 2026×3AAAI 2026×2ICML 2026社会与人本评测与基准可解释性多模态推理
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0 一作 · 6 篇 · ACL 2026 / AAAI 2026 / ICML 2026

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2026 顶会论文

6
作者位次 #5/5ACL 2026Poster· Natural Language Processing

VIGNETTE : Socially Grounded Bias Evaluation for Vision-Language Models

Chahat Raj · Bowen Wei · Aylin Caliskan · Antonios Anastasopoulos · Ziwei Zhu

摘要

While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less attention. Existing VLM bias studies often focus on portrait-style images and gender-occupation associations, overlooking broader and more complex social...

多模态评测与基准社会与人本OpenReview 项目页
作者位次 #6/6ACL 2026Findings· Findings of ACL

Talent or Luck? Evaluating Attribution Bias in Large Language Models

Chahat Raj · Mahika Banerjee · Jinhao Pan · Aylin Caliskan · Antonios Anastasopoulos · Ziwei Zhu

摘要

When a student fails an exam, do we tend to blame their effort or the test’s difficulty? Attribution, defined as how reasons are assigned to event outcomes, shapes perceptions, reinforces stereotypes, and influences decisions. Attribution Theory explains how people attribute causes to internal fa...

社会与人本评测与基准推理OpenReview 项目页
作者位次 #3/3ACL 2026Findings· Findings of ACL

Inject to Heal: Alleviating hallucination in LVLM s via Context Embedding Injection

Mehrdad Fazli · Bowen Wei · Ziwei Zhu

摘要

Hallucinations—generating responses inconsistent with the visual input—remain a critical limitation of large vision-language models (LVLMs), especially in open-ended tasks such as image captioning and visual reasoning. In this work, we probe the layer-wise generation dynamics that drive hallucina...

多模态可解释性评测与基准OpenReview 项目页
作者位次 #3/3AAAI 2026Poster· Artificial Intelligence

Making Sense of LLM Decisions: A Prototype-based Framework for Explainable Classification

Bowen Wei · Mehrdad Fazli · Ziwei Zhu

作者位次 #3/3AAAI 2026Poster· Artificial Intelligence

Bias Association Discovery Framework for Open-Ended LLM Generations

Jinhao Pan · Chahat Raj · Ziwei Zhu

社会与人本OpenReview

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