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Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures
Shenran Wang, Timothy Tin-Long Tse, Jian Zhu
We perform in-depth evaluations of in-context learning (ICL) on state-of-the-art transformer, state-space, and hybrid large language models over two categories of knowledge-based I…
Internalizing ASR with Implicit Chain of Thought for Efficient Speech-to-Speech Conversational LLM
Robin Shing-Hei Yuen, Timothy Tin-Long Tse, Jian Zhu
Current speech-based LLMs are predominantly trained on extensive ASR and TTS datasets, excelling in tasks related to these domains. However, their ability to handle direct speech-t…
Intermittent Semi-Working Mask: A New Masking Paradigm for LLMs
HaoYuan Hu, Mingcong Lu, Di Luo +10
Multi-turn dialogues and context-intensive tasks challenge Large Language Models (LLMs) to integrate long histories without sacrificing generation quality. Although prefix LLMs can…