3 citations · 3 across the 5 of their papers we have counts for
6 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization
Yichen Yan, Ming Zhong, Qi Zhu +3
Multimodal large language models (MLLMs) rely heavily on instruction tuning to align vision and language capabilities, yet the computational cost of training on large-scale dataset…
The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang
Fenghua Liu, Yulong Chen, Yixuan Liu +3
Large Language Models (LLMs) achieve gold-medal performance across many benchmarks, yet it remains unclear whether such success reflects genuine reasoning or pattern matching. From…
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
An Luo, Xun Xian, Jin Du +12
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human…
Deep Learning Based Dense Retrieval: A Comparative Study
Ming Zhong, Zhizhi Wu, Nanako Honda
Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but their robustness against tokenizer poisoning remains underexplored. In this…
Why Does the Effective Context Length of LLMs Fall Short?
Chenxin An, Jun Zhang, Ming Zhong +5
Advancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs). However, recent work r…