Hugging Face, an AI startup, has raised a massive $235 million in funding from big tech companies like Google, Amazon, and IBM, highlighting the demand for collaborative AI development. The Anti-hype LLM reading list is a valuable resource for anyone interested in understanding language models from a practical perspective, providing links to reasonable and good explanations of how things work, with no hype or vendor content. Large language models are being utilized in the development of autonomous agents, which can better understand natural language, generate human-like responses, and adapt to new situations. The Giraffe paper explores the limitations of fixed context lengths in large language models and introduces new strategies for context length extrapolation, which can greatly improve their performance on longer sequences.
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Timestamps:
00:34 Introduction
02:05 Huggingface Receives $235M in funding from big tech
03:59 Anti-hype LLM reading list
05:26 Tweet by Cameron R. Wolfe
06:39 Fake sponsor
08:45 A Survey on Large Language Model based Autonomous Agents
10:23 Instruction Tuning for Large Language Models: A Survey
11:46 Giraffe: Adventures in Expanding Context Lengths in LLMs
13:54 Outro

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