activity
20212026
most citedCodeT: Code Generation with Generated Tests

65 citations · 98 across the 16 of their papers we have counts for

collaborators
Showing cs.CLShow all

7 papers · 1 filter

cs.CL2026

DeepInnovator: Triggering the Innovative Capabilities of LLMs

Tianyu Fan, Fengji Zhang, Yuxiang Zheng +5

The application of Large Language Models (LLMs) in accelerating scientific discovery has garnered increasing attention, with a key focus on constructing research agents endowed wit…

cs.CL2025

ASearch: Ambiguity-Aware Question Answering with Reinforcement Learning

Fengji Zhang, Xinyao Niu, Chengyang Ying +7

Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models stil…

cs.CL2024★ 3 cited

CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark

Ge Zhang, Xinrun Du, Bei Chen +19

As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger g…

cs.CL2023★ 4 cited

Uncovering and Quantifying Social Biases in Code Generation

Yan Liu, Xiaokang Chen, Yan Gao +6

With the popularity of automatic code generation tools, such as Copilot, the study of the potential hazards of these tools is gaining importance. In this work, we explore the socia…

cs.CL2023★ 9 cited

RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation

Fengji Zhang, Bei Chen, Yue Zhang +6

The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository. While for automated code completion tools, it…

cs.CL2022★ 65 cited

CodeT: Code Generation with Generated Tests

Bei Chen, Fengji Zhang, Anh Nguyen +4

The task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samp…

cs.CL2021

Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal information

Fengji Zhang, Xiao Yu, Jacky Keung +5

Context: Stack Overflow is very helpful for software developers who are seeking answers to programming problems. Previous studies have shown that a growing number of questions are…