6 citations · 12 across the 15 of their papers we have counts for
17 papers
Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence
Xuanle Zhao, Qiushi Sun, Jingyu Xiao +16
While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual artifacts such as screenshots, cha…
CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability
Xianzhen Luo, Jingyuan Zhang, Shiqi Zhou +8
Evaluating and improving the security capabilities of code agents requires high-quality, executable vulnerability tasks. However, existing works rely on costly, unscalable manual r…
Automated Snippet-Alignment Data Augmentation for Code Translation
Zhiming Zhang, Qingfu Zhu, Xianzhen Luo +3
Code translation aims to translate the code from its source language to the target language and is used in various software development scenarios. Recent developments in Large Lang…
How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective
Xianzhen Luo, Jinyang Huang, Wenzhen Zheng +5
Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, u…
Scaling Laws for Code: A More Data-Hungry Regime
Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu +5
Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantly analyzed on Natural Language (…
Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
StepFun, :, Bin Wang +195
Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hard…
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
Dingzirui Wang, Xuanliang Zhang, Rongyu Cao +8
Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning…
Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
Zhiyuan Ma, Jiayu Liu, Xianzhen Luo +3
Empowering large language models (LLMs) with effective tool utilization capabilities is crucial for enabling AI agents to solve complex problems. However, current models face two m…
Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +8
Code Sensitivity refers to the ability of Code LLMs to recognize and respond to details changes in problem descriptions. While current code benchmarks and instruction data focus on…
ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
Xuanle Zhao, Xuexin Liu, Haoyue Yang +5
Although multimodal large language models (MLLMs) show promise in generating chart rendering code, editing charts via code presents a greater challenge. This task demands MLLMs to…
Is Compression Really Linear with Code Intelligence?
Shijie Xuyang, Xianzhen Luo, Zheng Chu +6
Understanding the relationship between data compression and the capabilities of Large Language Models (LLMs) is crucial, especially in specialized domains like code intelligence. P…
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
Xuanle Zhao, Xianzhen Luo, Qi Shi +4
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks. However, interpreting charts with textual descriptions often leads…
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
Siming Huang, Tianhao Cheng, J. K. Liu +16
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are in…
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling
Xianzhen Luo, Yixuan Wang, Qingfu Zhu +4
Massive parameters of LLMs have made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-ver…
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
Yixuan Wang, Xianzhen Luo, Fuxuan Wei +6
Existing speculative decoding methods typically require additional model structure and training processes to assist the model for draft token generation. This makes the migration o…
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +4
Instruction tuning plays a pivotal role in Code Large Language Models (Code LLMs) for the task of program synthesis. Presently, two dominant paradigms for collecting tuning data ar…
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +5
Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. Current…