1 citations · 1 across the 8 of their papers we have counts for
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Large Language Models Do Not Always Need Readable Language
Jiayi Zhu, Haoxuan Peng, Junxi Wang +3
Large language models (LLMs) are commonly prompted and interfaced with human-readable natural language, even when the intended reader is another model. This paper investigates whet…
TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
Weiya Li, Junjie Chen, Bei Li +8
Machine translation has long been a central task in natural language processing. With the rapid advancement of large language models (LLMs), there has been remarkable progress in t…
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning
Shaobo Wang, Xiangqi Jin, Ziming Wang +8
Fine-tuning large language models (LLMs) on task-specific data is essential for their effective deployment. As dataset sizes grow, efficiently selecting optimal subsets for trainin…
Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem?
Zichen Wen, Yifeng Gao, Weijia Li +2
Multimodal large language models (MLLMs) have shown remarkable performance for cross-modal understanding and generation, yet still suffer from severe inference costs. Recently, abu…
Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More
Zichen Wen, Yifeng Gao, Shaobo Wang +5
Vision tokens in multimodal large language models often dominate huge computational overhead due to their excessive length compared to linguistic modality. Abundant recent methods…