6 papers · 1 filter
Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
Wenkai Yang, Shuming Ma, Yankai Lin +1
Recent studies have shown that making a model spend more time thinking through longer Chain of Thoughts (CoTs) enables it to gain significant improvements in complex reasoning task…
BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
Hongyu Wang, Shuming Ma, Furu Wei
Efficient deployment of 1-bit Large Language Models (LLMs) is hindered by activation outliers, which complicate quantization to low bit-widths. We introduce BitNet v2, a novel fram…
BitNet a4.8: 4-bit Activations for 1-bit LLMs
Hongyu Wang, Shuming Ma, Furu Wei
Recent research on the 1-bit Large Language Models (LLMs), such as BitNet b1.58, presents a promising direction for reducing the inference cost of LLMs while maintaining their perf…
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Hongyu Wang, Shuming Ma, Ruiping Wang +1
We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can…
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models
Tianyi Tang, Wenyang Luo, Haoyang Huang +5
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. It remains a challenging…
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
Tianyi Tang, Hongyuan Lu, Yuchen Eleanor Jiang +5
Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgeme…