2 citations · 2 across the 4 of their papers we have counts for
5 papers
Diverse Thinking Schemata Elicit Better Reasoning in Large Language Models
Xinyue Liang, Yizhe Yang, Yu Bai +3
Large reasoning models (LRMs) have attracted increasing attention for their ability to solve complex mathematical problems by generating extended reasoning chains. In this work, we…
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
NVIDIA, :, Amala Sanjay Deshmukh +204
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios
Bin Xu, Yu Bai, Huashan Sun +10
As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing…
Extrapolation Merging: Keep Improving With Extrapolation and Merging
Yiguan Lin, Bin Xu, Yinghao Li +1
Large Language Models (LLMs) require instruction fine-tuning to perform different downstream tasks. However, the instruction fine-tuning phase still demands significant computation…
SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree Search for Code Generation
Bin Xu, Yiguan Lin, Yinghao Li +1
Large language models demonstrate exceptional performance in simple code generation tasks but still face challenges in tackling complex problems. These challenges may stem from ins…