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20232025
most citedQwen2.5 Technical Report

107 citations · 199 across the 20 of their papers we have counts for

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5 papers · 1 filter

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

AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models

Qin Zhu, Fei Huang, Runyu Peng +6

While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are v…

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…