4 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
CoinMath: Harnessing the Power of Coding Instruction for Math LLMs
Chengwei Wei, Bin Wang, Jung-jae Kim +2
Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. However, the best practice to…
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models
Ayrton San Joaquin, Bin Wang, Zhengyuan Liu +4
Despite advancements, fine-tuning Large Language Models (LLMs) remains costly due to the extensive parameter count and substantial data requirements for model generalization. Acces…
CRAFT: Extracting and Tuning Cultural Instructions from the Wild
Bin Wang, Geyu Lin, Zhengyuan Liu +2
Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications. Despite their wide use cases, their understanding of…
CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment
Geyu Lin, Bin Wang, Zhengyuan Liu +1
Multilingual proficiency presents a significant challenge for large language models (LLMs). English-centric models are usually suboptimal in other languages, particularly those tha…
Resilience of Large Language Models for Noisy Instructions
Bin Wang, Chengwei Wei, Zhengyuan Liu +2
As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating tex…