Analogical Prompting of Large Language Models - Webdesk AI News

Drawing inspiration from human cognition, particularly analogical reasoning, where we use prior knowledge to tackle new challenges

The advancement in Large Language Models (LLMs) has been nothing short of remarkable. They've shown unprecedented capabilities across diverse tasks. However, a recent study introduced a new method for enhancing their performance: analogical prompting. This method draws inspiration from human cognition, particularly analogical reasoning, where we use prior knowledge to tackle new challenges. Essentially, when given a problem, the model is prompted to self-generate relevant exemplars or previous similar cases, and then use them as a basis for solving the current problem.

Traditional chain-of-thought (CoT) prompting has demonstrated the potential of LLMs in solving complex tasks. For example, in solving mathematical problems, these models would be prompted to generate step-by-step reasoning. While effective, this method poses challenges, especially when it comes to providing relevant guidance without manual labeling of reasoning exemplars. This is where analogical prompting comes in. It allows models to recall how they've resolved similar issues in the past and apply those solutions to new problems. This approach was expressed in research paper by Michihiro Yasunaga, Xinyun Chen, Yujia Li, Panupong Pasupat, Jure Leskovec, Percy Liang, Ed H. Chi, and Denny Zhou entitled "Large Language Models as Analogical Reasoners."

The brilliance behind analogical prompting lies in its automated approach. For instance, instead of providing the model with labeled examples of how to solve a problem, the LLM is prompted to recall or generate relevant examples itself. This is achieved by giving the model a single, continuous prompt that guides it to first generate relevant examples, and then solve the given problem. Experimental results have been promising, showing that this approach consistently outperforms the traditional CoT methods.

A further enhancement to the method is the introduction of self-generated knowledge alongside exemplars. Recognizing that simply generating examples might not suffice for complex tasks, the model is also prompted to generate high-level takeaways or "knowledge" about the problem. This allows it to understand the core concepts of a task before attempting a solution, improving the overall quality and relevance of its answers.

In conclusion, analogical prompting, inspired by human reasoning processes, offers a novel and efficient way to harness the power of LLMs. By allowing these models to generate their own exemplars and knowledge, we are one step closer to creating AI systems that can reason, learn, and adapt in ways previously thought exclusive to human cognition.