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Thinking LLMs
🤔 Thinking LLMs: General Instruction Following with Thought Generation
This research paper explores the concept of "Thinking LLMs," or large language models that can generate internal thoughts before responding to user prompts. The authors propose a training method called Thought Preference Optimization (TPO) which uses an iterative process to encourage LLMs to develop thinking abilities. TPO leverages an existing judge model that evaluates responses, implicitly guiding the model to improve its thoughts based on the quality of the resulting responses. The study demonstrates that Thinking LLMs can outperform standard LLMs on various general instruction-following tasks, including those not typically associated with reasoning, such as marketing and health. The research highlights the potential for Thinking LLMs to expand the capabilities of these models beyond traditional reasoning and problem-solving domains.
📎 Link to paper
This research paper explores the concept of "Thinking LLMs," or large language models that can generate internal thoughts before responding to user prompts. The authors propose a training method called Thought Preference Optimization (TPO) which uses an iterative process to encourage LLMs to develop thinking abilities. TPO leverages an existing judge model that evaluates responses, implicitly guiding the model to improve its thoughts based on the quality of the resulting responses. The study demonstrates that Thinking LLMs can outperform standard LLMs on various general instruction-following tasks, including those not typically associated with reasoning, such as marketing and health. The research highlights the potential for Thinking LLMs to expand the capabilities of these models beyond traditional reasoning and problem-solving domains.
📎 Link to paper
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By Shahriar Shariati🤔 Thinking LLMs: General Instruction Following with Thought Generation
This research paper explores the concept of "Thinking LLMs," or large language models that can generate internal thoughts before responding to user prompts. The authors propose a training method called Thought Preference Optimization (TPO) which uses an iterative process to encourage LLMs to develop thinking abilities. TPO leverages an existing judge model that evaluates responses, implicitly guiding the model to improve its thoughts based on the quality of the resulting responses. The study demonstrates that Thinking LLMs can outperform standard LLMs on various general instruction-following tasks, including those not typically associated with reasoning, such as marketing and health. The research highlights the potential for Thinking LLMs to expand the capabilities of these models beyond traditional reasoning and problem-solving domains.
📎 Link to paper
This research paper explores the concept of "Thinking LLMs," or large language models that can generate internal thoughts before responding to user prompts. The authors propose a training method called Thought Preference Optimization (TPO) which uses an iterative process to encourage LLMs to develop thinking abilities. TPO leverages an existing judge model that evaluates responses, implicitly guiding the model to improve its thoughts based on the quality of the resulting responses. The study demonstrates that Thinking LLMs can outperform standard LLMs on various general instruction-following tasks, including those not typically associated with reasoning, such as marketing and health. The research highlights the potential for Thinking LLMs to expand the capabilities of these models beyond traditional reasoning and problem-solving domains.
📎 Link to paper