2024/10/05 by Eunseong Choi, Choi, Eunseong, Sunkyung Lee +6 · 4 citations
Arts and Humanities · Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Digital Humanities and Scholarship #FOS: Computer and information sciences #I.2.7 #Mathematics, Computing, and Information Processing
paper · pdf · doi:10.48550/arxiv.2410.04139
openalex publication_date 2024/10/05 · openalex created_date 2024/11/01 · openalex updated_date 2026/07/28
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%.