activity
20232025
most citedQwen Technical Report

110 citations · 298 across the 14 of their papers we have counts for

collaborators

14 papers

astro-ph.IM2025★ 1 cited

AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy

Jinghang Shi, Xiaoyu Tang, Yang Huang +4

Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…

cs.CL2025

InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior

Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3

Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…

cs.AI2025

MARGE: Improving Math Reasoning for LLMs with Guided Exploration

Jingyue Gao, Runji Lin, Keming Lu +3

Large Language Models (LLMs) exhibit strong potential in mathematical reasoning, yet their effectiveness is often limited by a shortage of high-quality queries. This limitation nec…

cs.CL2025

WorldPM: Scaling Human Preference Modeling

Binghai Wang, Runji Lin, Keming Lu +17

Motivated by scaling laws in language modeling that demonstrate how test loss scales as a power law with model and dataset sizes, we find that similar laws exist in preference mode…

cs.CL2025★ 107 cited

Qwen2.5 Technical Report

Qwen, :, An Yang +41

In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been sign…

cs.CL2025

The Lessons of Developing Process Reward Models in Mathematical Reasoning

Zhenru Zhang, Chujie Zheng, Yangzhen Wu +6

Process Reward Models (PRMs) emerge as a promising approach for process supervision in mathematical reasoning of Large Language Models (LLMs), which aim to identify and mitigate in…

cs.AI2024★ 1 cited

ProcessBench: Identifying Process Errors in Mathematical Reasoning

Chujie Zheng, Zhenru Zhang, Beichen Zhang +6

As language models regularly make mistakes when solving math problems, automated identification of errors in the reasoning process becomes increasingly significant for their scalab…

cs.CL2024★ 15 cited

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…

cs.LG2024

Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence

Luo Ji, Runji Lin

Interactive artificial intelligence in the motion control field is an interesting topic, especially when universal knowledge is adaptive to multiple tasks and universal environment…

cs.CL2024★ 60 cited

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…

cs.CL2024

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback

Bofei Gao, Zefan Cai, Runxin Xu +10

In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models. However, exi…

cs.CL2024

Online Merging Optimizers for Boosting Rewards and Mitigating Tax in Alignment

Keming Lu, Bowen Yu, Fei Huang +3

Effectively aligning Large Language Models (LLMs) with human-centric values while preventing the degradation of abilities acquired through Pre-training and Supervised Fine-tuning (…

cs.CL2023★ 4 cited

Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

Keming Lu, Hongyi Yuan, Runji Lin +4

The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs…

cs.CL2023★ 110 cited

Qwen Technical Report

Jinze Bai, Shuai Bai, Yunfei Chu +45

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…