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Large Language Models Can Reason With Help

Large language models (LLMs) are a powerful new technology, capable of generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way. However, one of their weaknesses is that they can struggle with complex reasoning problems.

A recent technique called “chain-of-thought” (CoT) prompting has helped to improve LLMs’ reasoning abilities. CoT prompting works by encouraging the LLM to break down a complex reasoning problem into a series of smaller steps. This allows the LLM to solve the problem more systematically, leading to better results.

However, one of the limitations of CoT prompting is that it can be time-consuming to create the step-by-step reasoning demonstrations that are needed to train the LLM. To address this, researchers have developed a new method called “self-harmonized chain-of-thought” (ECHO) that automatically generates these demonstrations.

ECHO works by first clustering similar questions together. Then, the model generates a reasoning chain for a representative question from each cluster using a technique called “zero-shot-CoT” which encourages the model to reason step-by-step without needing specific examples. Finally, ECHO uses a dynamic prompting method to improve the quality of the reasoning chains by iteratively refining them based on the reasoning chains from other questions.

For example, consider the following question: “There are 15 trees in a grove. After some more trees are planted, there will be 21 trees. How many trees were planted?”.

ECHO would first use zero-shot-CoT to generate a reasoning chain for this question, such as: “Let’s think step-by-step. First, we need to find the difference between the number of trees after the planting and the original number of trees. To do this, we can subtract the original number of trees from the current number of trees: 21 - 15 = 6. Therefore, 6 trees were planted.”

ECHO would then use this reasoning chain, along with similar reasoning chains for other questions, to improve the quality of its reasoning chains for future questions. This iterative process helps to ensure that the reasoning chains are consistent and effective across a variety of tasks.

The researchers found that ECHO outperformed other CoT methods in three reasoning domains: arithmetic, commonsense, and symbolic reasoning. This suggests that ECHO is a promising new approach to improving LLMs’ reasoning abilities.

Overall, ECHO is an exciting new development in the field of LLMs. This method has the potential to significantly improve LLMs’ reasoning abilities, making them even more powerful and versatile tools. As the field of LLMs continues to develop, it will be interesting to see how ECHO and other CoT techniques are used to further improve the capabilities of these powerful models.