52 citations · 244 across the 20 of their papers we have counts for
47 papers
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
An Yang, Beichen Zhang, Binyuan Hui +13
In this report, we present a series of math-specific large language models: Qwen2.5-Math and Qwen2.5-Math-Instruct-1.5B/7B/72B. The core innovation of the Qwen2.5 series lies in in…
mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding
Anwen Hu, Haiyang Xu, Liang Zhang +6
Multimodel Large Language Models(MLLMs) have achieved promising OCR-free Document Understanding performance by increasing the supported resolution of document images. However, this…
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model
Chenhan Yuan, Fei Huang, Ru Peng +4
Transformer-based large language models (LLMs) exhibit limitations such as generating unsafe responses, unreliable reasoning, etc. Existing inference intervention approaches attemp…
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
Jiabo Ye, Haiyang Xu, Haowei Liu +6
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in executing instructions for a variety of single-image tasks. Despite this progress, significan…
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models
Guanting Dong, Keming Lu, Chengpeng Li +4
One core capability of large language models (LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhan…
Qwen2 Technical Report
An Yang, Baosong Yang, Binyuan Hui +59
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruct…