2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models
YiFan Zhang, Shanglin Lei, Runqi Qiao +10
The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to compreh…
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
Yejie Wang, Keqing He, Dayuan Fu +11
Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit hi…
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
Runqi Qiao, Qiuna Tan, Guanting Dong +15
Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks,…
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
Xiaoshuai Song, Muxi Diao, Guanting Dong +13
Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on b…