This research paper describes a new method called Forest-of-Thought (FoT) designed to help large language models (LLMs) solve problems better. LLMs, like the ones that power chatbots, are good at language tasks but struggle with complex reasoning. FoT works by using multiple “thinking trees” to explore different ways to solve a problem. Imagine each tree representing a different approach to finding the answer. By combining the results from these trees, FoT gets a more complete picture and makes better decisions. The researchers tested FoT on math problems and found that it significantly improves accuracy compared to existing methods. This is because FoT allows the model to consider multiple perspectives, correct its mistakes, and learn from its past errors. In simple terms, FoT helps LLMs become smarter problem solvers by thinking more like humans.
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