Papers by Taeyoun Kim

1 papers
Mitigating Bias in RAG: Controlling the Embedder (2025.findings-acl)

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Challenge: a promising modular AI system enhances factuality and privacy in large language models . however, each component introduces its own biases into the RAG system, which could cause representational harm and unsafe user interactions.
Approach: They study the conflict between biases of each component and their relationship to the overall bias of the retrieval augmented generation system.
Outcome: The proposed model can be controlled by the embedder while maintaining utility . the proposed model is more fair than existing models, the authors show .

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