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cs.CL2025
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Liliang Ren, Yang Liu, Yadong Lu +3
Efficiently modeling sequences with infinite context length has long been a challenging problem. Previous approaches have either suffered from quadratic computational complexity or…
cs.CL2024
StreamAdapter: Efficient Test Time Adaptation from Contextual Streams
Dilxat Muhtar, Yelong Shen, Yaming Yang +11
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks directly from the given demonstrations without requiring gradient updates. While recent advances…
cs.CL2024
Adapting LLM Agents with Universal Feedback in Communication
Kuan Wang, Yadong Lu, Michael Santacroce +3
Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-lingui…