3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMs
Ying Liu, Can Li, Ting Zhang +4
The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. While prior research has primarily examined their…
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
NVIDIA, :, Aarti Basant +214
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compar…
From Answers to Questions: EQGBench for Evaluating LLMs' Educational Question Generation
Chengliang Zhou, Mei Wang, Ting Zhang +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in mathematical problem-solving. However, the transition from providing answers to generating high-quality ed…
Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning
Xuetao Ma, Wenbin Jiang, Hua Huang
In-context learning (ICL) can significantly enhance the complex reasoning capabilities of large language models (LLMs), with the key lying in the selection and ordering of demonstr…
CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
Zheqi He, Xinya Wu, Pengfei Zhou +5
Multi-modal large language models(MLLMs) have achieved remarkable progress and demonstrated powerful knowledge comprehension and reasoning abilities. However, the mastery of domain…