39 citations · 188 across the 22 of their papers we have counts for
22 papers
Can Programming Languages Boost Each Other via Instruction Tuning?
Daoguang Zan, Ailun Yu, Bo Shen +8
When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programmin…
SoTaNa: The Open-Source Software Development Assistant
Ensheng Shi, Fengji Zhang, Yanlin Wang +6
Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an ef…
Private-Library-Oriented Code Generation with Large Language Models
Daoguang Zan, Bei Chen, Yongshun Gong +6
Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. Th…
How Do In-Context Examples Affect Compositional Generalization?
Shengnan An, Zeqi Lin, Qiang Fu +4
Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence. The AI community mainly studies this…
Question Answering as Programming for Solving Time-Sensitive Questions
Xinyu Zhu, Cheng Yang, Bei Chen +3
Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature o…
Skill-Based Few-Shot Selection for In-Context Learning
Shengnan An, Bo Zhou, Zeqi Lin +5
In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each…