Papers by Keunha Kim
ConvX: A Lightweight Converter to Bridge Indexed Dense Representations and Large Language Models for Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing RAG pipelines suffer from critical efficiency limitations due to their complexity and complexity. |
| Approach: | They propose a compression-based RAG framework that directly leverages indexed dense representations produced by a retriever, substituting to long text contexts. |
| Outcome: | Empirical results show that the proposed model achieves competitive performances compared to the state-of-the-art model that uses a large ad-hoc context compressor while offering substantially improved inference efficiency. |