Innovation
Present cutting-edge research, prototypes, or advanced methodologies
that push the boundaries of LLMs. We are looking for RAG, beyond RAG, distributed
training and evaluation, theoretical analysis of how LLMs work, hardware innovation,
and future technologies.
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