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Recent research by Google DeepMind, University College London, Google Research, and Tel Aviv University has examined the reasoning capabilities of large language models (LLMs) when faced with complex, multi-hop questions without explicit Chain-of-Thought (CoT) prompting. The study highlights ongoing challenges in LLM reasoning. Additionally, Meta has introduced a new paradigm called Chain of Continuous Thought (COCONUT), which allows LLMs to reason in a continuous latent space instead of relying solely on natural language. This approach utilizes the last hidden state of the LLM as the reasoning state, potentially enhancing the models' ability to process complex reasoning tasks. The research also explores the internal workings of CoT prompting in LLMs, focusing on aspects such as decoding behavior and projection space changes.
Training LLMs to Reason in a Continuous Latent Space Meta presents Coconut (Chain of Continuous Thought), a novel paradigm that enables LLMs to reason in continuous latent space rather than natural language. https://t.co/2py3AfbH25
Inside the black box of Chain-of-Thought (CoT). How CoT helps LLMs combine format patterns with deep knowledge. This paper investigates how Chain-of-Thought (CoT) prompting works internally in LLMs by examining three aspects: decoding behavior, projection space changes, and… https://t.co/JpMDkcS8Em
Training LLMs to Reason in a Continuous Latent Space Meta presents Coconut (Chain of Continuous Thought), a novel paradigm that enables LLMs to reason in continuous latent space rather than natural language. Coconut takes the last hidden state of the LLM as the reasoning state… https://t.co/MY5PJYrzRU
