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Training Large Language Models to Reason in Continuous Latent Space
Manage episode 461129295 series 3448051
LLMs have typically been restricted to reason in the "language space," where chain-of-thought (CoT) is used to solve complex reasoning problems. But a new paper argues that language space may not always be the best for reasoning. In this paper read, we cover an exciting new technique from a team at Meta called Chain of Continuous Thought—also known as "Coconut." In the paper, "Training Large Language Models to Reason in a Continuous Latent Space" explores the potential of allowing LLMs to reason in an unrestricted latent space instead of being constrained by natural language tokens.
Read a full breakdown of Coconut on our blog
Learn more about AI observability and evaluation in our course, join the Arize AI Slack community or get the latest on LinkedIn and X.
41 つのエピソード
Manage episode 461129295 series 3448051
LLMs have typically been restricted to reason in the "language space," where chain-of-thought (CoT) is used to solve complex reasoning problems. But a new paper argues that language space may not always be the best for reasoning. In this paper read, we cover an exciting new technique from a team at Meta called Chain of Continuous Thought—also known as "Coconut." In the paper, "Training Large Language Models to Reason in a Continuous Latent Space" explores the potential of allowing LLMs to reason in an unrestricted latent space instead of being constrained by natural language tokens.
Read a full breakdown of Coconut on our blog
Learn more about AI observability and evaluation in our course, join the Arize AI Slack community or get the latest on LinkedIn and X.
41 つのエピソード
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