most citedDDK: Distilling Domain Knowledge for Efficient Large Language Models

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2025★ 1 cited

From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence

Jian Yang, Xianglong Liu, Weifeng Lv +68

Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…

cs.CL2025

Multilingual Multimodal Software Developer for Code Generation

Linzheng Chai, Jian Yang, Shukai Liu +12

The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting crucial visual aids like diagrams and…

cs.CL2024

M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation

Jiaheng Liu, Ken Deng, Congnan Liu +13

Repository-level code completion has drawn great attention in software engineering, and several benchmark datasets have been introduced. However, existing repository-level code com…

cs.CL2024★ 4 cited

DDK: Distilling Domain Knowledge for Efficient Large Language Models

Jiaheng Liu, Chenchen Zhang, Jinyang Guo +13

Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands. Knowledge Distil…

cs.CL2024★ 1 cited

R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models

Ken Deng, Jiaheng Liu, He Zhu +13

Code completion models have made significant progress in recent years. Recently, repository-level code completion has drawn more attention in modern software development, and sever…