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Afrozah Nadeem

ACL 2026×2社会与人本多语言与语言学推理可解释性效率与系统
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2 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

Framing Political Bias in Multilingual LLM s Across P akistani Languages

Afrozah Nadeem · Mark Dras · Usman Naseem

摘要

Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of economic and political bias have focused on high-resource Western languages and contexts. This leaves a blind spots in low-resource, multilingual regions such as Pakistan, where linguistic identity is closel...

社会与人本多语言与语言学推理OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Fairness Evaluation and Inference Level Mitigation in LLM s

Afrozah Nadeem · Mark Dras · Usman Naseem

摘要

Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, and the propagation of unwanted patterns during extended dialogue and conversations. Although training-time or data-ce...

可解释性效率与系统检索与知识OpenReview 项目页

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