7 papers
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
Hongcheng Guo, Wei Zhang, Junhao Chen +9
Recently advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of the robust benc…
Self-Evolving Critique Abilities in Large Language Models
Zhengyang Tang, Ziniu Li, Zhenyang Xiao +8
Despite their remarkable performance, Large Language Models (LLMs) face a critical challenge: providing feedback for tasks where human evaluation is difficult or where LLMs potenti…
Multi-Agent Collaboration for Multilingual Code Instruction Tuning
Jian Yang, Wei Zhang, Jiaxi Yang +9
Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-…
RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques
Zhengyang Tang, Ziniu Li, Zhenyang Xiao +8
Critiques are important for enhancing the performance of Large Language Models (LLMs), enabling both self-improvement and constructive feedback for others by identifying flaws and…
Qwen2.5-Coder Technical Report
Binyuan Hui, Jian Yang, Zeyu Cui +21
In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes six models: Qwen2.5-Coder-(0.5B/1.5B/3B/7B/14B/…
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Bofei Gao, Feifan Song, Yibo Miao +22
Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This align…