Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning (2024.emnlp-main)
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| Challenge: | Recent studies highlight the effectiveness of using in-context learning (ICL) to steer large language models in processing tabular data. |
| Approach: | They propose a method that uses clustering and evolutionary strategies to curate a representative sample set from training data. |
| Outcome: | The proposed method significantly improves fairness across various metrics, showing its efficacy in real-world scenarios. |
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Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang
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Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning (2025.emnlp-main)
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| Challenge: | In-context learning (ICL) is a critical emerging capability of large language models (LLMs), enabling few-shot learning during inference by including a few demonstrations in the prompt. |
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