LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education (2025.findings-naacl)
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| Challenge: | Existing studies have shown that relying on LLMs as information providers may hurt student learning. |
| Approach: | They introduce and apply two bias score metrics to evaluate LLMs for bias in the personalized educational setting, specifically on the models’ roles as “teachers.” |
| Outcome: | The proposed models harm student learning by perpetuating harmful stereotypes and reversing them. |
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| Challenge: | Recent studies suggest using large language models to make tabular classifications . however, LLMs have been shown to exhibit harmful social biases based on stereotypes and inequalities present in society. |
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Beyond Performance: Quantifying and Mitigating Label Bias in LLMs (2024.naacl-long)
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| Challenge: | Large language models exhibit undesirable preference toward predicting certain answers over others, despite their adaptability to diverse tasks. |
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
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A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)
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| Challenge: | Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias. |
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| Challenge: | Large Language Models (LLMs) have potential to automate hiring but inherent biases may lead to unfair hiring practices. |
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Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection (2025.findings-acl)
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| Challenge: | Existing methods to quantify and quantify social biases in Large Language Models (LLMs) focus on explicit bias, with little attention to implicit bias. |
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Biased LLMs can Influence Political Decision-Making (2025.acl-long)
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Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
| Challenge: | Recent studies have found that biased LLMs can influence decisions in areas such as medical classifications and educational hiring. |
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LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs (2025.naacl-long)
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Do Xuan Long, Ngoc-Hai Nguyen, Tiviatis Sim, Hieu Dao, Shafiq Joty, Kenji Kawaguchi, Nancy F. Chen, Min-Yen Kan
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Humans or LLMs as the Judge? A Study on Judgement Bias (2024.emnlp-main)
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| Challenge: | Proprietary models such as GPT-4, Claude, Gemini-Pro and others are being democratized to improve evaluations of LLMs. |
| Approach: | They propose a framework that is free from referencing groundtruth annotations for investigating **Misinformation Oversight Bias**, **Gender Bia**,**Authority Bia* and **Beauty Bia's** on LLM and human judges. |
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Characterizing Positional Bias in Large Language Models: A Multi-Model Evaluation of Prompt Order Effects (2025.findings-emnlp)
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| Challenge: | Large Language Models can be influenced by various forms of biases, says a new study . positional bias affects how LLMs interpret and weigh information, the authors say . |
| Approach: | a new study examines the impact of positional bias on large language models . positional biased models prioritize items based on their position rather than content or quality . |
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