Papers by Jun Park
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have recently exhibited performance gains owing to a wide variety of prompting techniques, including Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT), and In-Context Learning (ICL). |
| Approach: | They propose a prompt compression method that captures the global context without compromising semantic consistency while detouring the necessity of pseudo-labels for training the compressor. |
| Outcome: | Empirical results show that the proposed method retains key contexts while reducing the prompt length by 80%. |