This program is tentative and subject to change.
Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recent advances enable code LLMs to process long inputs, high API costs and generation latency remain substantial bottlenecks. Existing context pruning techniques, such as LLMLingua, achieve promising results for general text but overlook code-specific structures and dependencies, leading to suboptimal performance in programming tasks. In this paper, we propose LongCodeZip, a novel plug-and-play code compression framework designed specifically for code LLMs. LongCodeZip employs a dual-stage strategy: (1) coarse-grained compression, which identifies and ranks function-level chunks using conditional perplexity with respect to the instruction, retaining only the most relevant functions; and (2) fine-grained compression, which segments retained functions into blocks based on perplexity and selects an optimal subset under an adaptive token budget to maximize relevance. Evaluations across multiple tasks, including code completion, summarization, and question answering, show that LongCodeZip consistently outperforms baseline methods, achieving up to a 5.6x compression ratio without degrading task performance. By effectively reducing context size while preserving essential information, LongCodeZip enables LLMs to better scale to real-world, large-scale code scenarios, advancing the efficiency and capability of code intelligence applications. Our code and data are available at https://github.com/YerbaPage/LongCodeZip.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | Coverage-Based Harmfulness Testing for LLM Code Transformation Research Papers Honghao Tan Concordia University, Haibo Wang Concordia University, Diany Pressato Concordia University, Yisen Xu Software PErformance, Analysis, and Reliability (SPEAR) lab, Concordia University, Montreal, Canada, Shin Hwei Tan Concordia University | ||
11:10 10mTalk | RealisticCodeBench: Towards More Realistic Evaluation of Large Language Models for Code Generation Research Papers Xiao Yu Zhejiang University, Haoxuan Chen Wuhan University of Technology, Lei Liu Xi’an Jiaotong University, Xing Hu Zhejiang University, Jacky Keung City University of Hong Kong, Xin Xia Zhejiang University | ||
11:20 10mTalk | Code-DiTing: Automatic Evaluation of Code Generation without References or Test Cases Research Papers Guang Yang , Yu Zhou Nanjing University of Aeronautics and Astronautics, Xiang Chen Nantong University, Wei Zheng Northwestern Polytechnical University, Xing Hu Zhejiang University, Xin Zhou Singapore Management University, Singapore, David Lo Singapore Management University, Taolue Chen Birkbeck, University of London Pre-print | ||
11:30 10mTalk | An Agent-based Evaluation Framework for Complex Code Generation Research Papers Xinchen Wang Harbin Institute of Technology, Pengfei Gao ByteDance, Chao Peng ByteDance, Ruida Hu Harbin Institute of Technology, Shenzhen, Cuiyun Gao Harbin Institute of Technology, Shenzhen | ||
11:40 10mTalk | PseudoFix: Refactoring Distorted Structures in Decompiled C Pseudocode Research Papers Gangyang Li University of Science and Technology of China, Xiuwei Shang University of Science and Technology of China, Shaoyin Cheng University of Science and Technology of China, junqi zhang University of Science and Technology of China, Li Hu , Xu Zhu University of Science and Technology of China, Weiming Zhang University of Science and Technology of China, Nenghai Yu School of Cyber Security, University of Science and Technology of China | ||
11:50 10mTalk | Evaluating and Improving Framework-based Parallel Code Completion with Large Language Models Research Papers Ke Liu , Qinglin Wang Shandong Normal University, Xiang Chen Nantong University, Guang Yang , YiGui Feng National University of Defense Technology, Gencheng Liu National University of Defense Technology, Jie Liu Institute of Software, Chinese Academy of Sciences | ||
12:00 10mTalk | Variational Prefix Tuning for diverse and accurate code summarization using pre-trained language models Journal-First Track Junda Zhao Department of Mechanical and Industrial Engineering, University of Toronto, Yuliang Song Department of Mechanical and Industrial Engineering, University of Toronto, Eldan Cohen Department of Mechanical and Industrial Engineering, University of Toronto | ||
12:10 10mTalk | Effective Code Membership Inference for Code Completion Models via Adversarial Prompts Research Papers Yuan Jiang Harbin Institute of Technology, Zehao Li Harbin Institute of Technology, Shan Huang East China Normal University, Christoph Treude Singapore Management University, Xiaohong Su Harbin Institute of Technology, Tiantian Wang Harbin Institute of Technology | ||
12:20 10mTalk | LongCodeZip: Compress Long Context for Code Language Models Research Papers Yuling Shi Shanghai Jiao Tong University, Yichun Qian Stanford University, Hongyu Zhang Chongqing University, Beijun Shen Shanghai Jiao Tong University, Xiaodong Gu Shanghai Jiao Tong University Pre-print Media Attached | ||