Jun Park


2024

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From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression
Eunseong Choi | Sunkyung Lee | Minjin Choi | Jun Park | Jongwuk Lee
Findings of the Association for Computational Linguistics: EMNLP 2024

Large language models (LLMs) have achieved significant performance gains using advanced prompting techniques over various tasks. However, the increasing length of prompts leads to high computational costs and often obscures crucial information. Prompt compression has been proposed to alleviate these issues, but it faces challenges in (i) capturing the global context and (ii) training the compressor effectively. To tackle these challenges, we introduce a novel prompt compression method, namely Reading To Compressing (R2C), utilizing the Fusion-in-Decoder (FiD) architecture to identify the important information in the prompt. Specifically, the cross-attention scores of the FiD are used to discern essential chunks and sentences from the prompt. R2C effectively captures the global context without compromising semantic consistency while detouring the necessity of pseudo-labels for training the compressor. Empirical results show that R2C retains key contexts, enhancing the LLM performance by 6% in out-of-domain evaluations while reducing the prompt length by 80%.

2008

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Using Confidence Vector in Multi-Stage Speech Recognition
Hyungbae Jeon | Kyuwoong Hwang | Hoon Chung | Seunghi Kim | Jun Park | Yunkeun Lee
Proceedings of the Workshop on Technologies and Corpora for Asia-Pacific Speech Translation (TCAST)