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. |
| Approach: | They propose a label bias calibration method that outperforms recent calibration approaches for improving performance and mitigating label bias. |
| Outcome: | The proposed method outperforms calibration approaches for improving performance and mitigating label bias. |
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Do Xuan Long, Ngoc-Hai Nguyen, Tiviatis Sim, Hieu Dao, Shafiq Joty, Kenji Kawaguchi, Nancy F. Chen, Min-Yen Kan
| Challenge: | Using format-following capabilities, state-of-the-art large language models (LLMs) can be leveraged to tailor outputs to specific task formats. |
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Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction. |
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Mitigating Label Biases for In-context Learning (2023.acl-long)
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| Challenge: | Existing methods to categorize label biases in in-context learning (ICL) have not addressed all three types of label bias. |
| Approach: | They propose a method that estimates a language model’s label bias using random in-domain words from the task corpus to categorize and detect label biases in ICL. |
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Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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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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The Impact of Inference Acceleration on Bias of LLMs (2025.naacl-long)
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| Challenge: | Recent work suggests strategies to increase inference efficiency with LLMs . however, these strategies may inadvertently lead to some side-effects. |
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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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Measuring and Mitigating Media Outlet Name Bias in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing studies have explored the potential political biases of large language models, but limited attention has been devoted to the effects of media outlet names. |
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Likelihood-based Mitigation of Evaluation Bias in Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. |
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Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications (2024.naacl-long)
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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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Conservative Bias in Large Language Models: Measuring Relation Predictions (2025.findings-acl)
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| Challenge: | Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, often defaulting to no_relation label when an appropriate option is unavailable. |
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