107 citations · 199 across the 20 of their papers we have counts for
5 papers · 2 filters
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Keming Lu, Hongyi Yuan, Runji Lin +4
The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs…
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
Shengguang Wu, Keming Lu, Benfeng Xu +3
Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a…
Speculative Contrastive Decoding
Hongyi Yuan, Keming Lu, Fei Huang +2
Large language models~(LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-opt…
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning
Chengpeng Li, Zheng Yuan, Hongyi Yuan +6
In math reasoning with large language models (LLMs), fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective, profoundly narr…
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
Guanting Dong, Hongyi Yuan, Keming Lu +7
Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, code generation, and instruction following. These…