Program-Aided Language Models

Although Large Language Models (LLMs) are used for a variety of applications, they have typically struggled to solve reasoning-based tasks. This issue was significantly diminished with the advent of prompting techniques like <a href="https://cameronrwolfe.substack.com/p/chain-of-thought-prompting-for-llms" rel="noopener ugc nofollow" target="_blank">Chain of Thought</a> and <a href="https://cameronrwolfe.substack.com/i/116166267/variants-of-cot-prompting" rel="noopener ugc nofollow" target="_blank">Least-to-Most prompting</a>. At a high level, these techniques encourage reasoning behavior in LLMs by providing examples of problem-solving rationales within the model’s prompt. Then, the model can learn to output such rationales and produce a step-by-step solution to the underlying problem. Notably, this is a prompting-only approach that requires no fine-tuning, revealing that LLMs are capable of reasoning given a prompt with sufficient context. Despite the effectiveness of techniques like chain of thought prompting, the LLM is expected to produce both a problem-solving chain of thought and a final answer. Interestingly, such an approach leads to peculiar failure cases in which the LLM may produce an accurate rationale for solving a problem but still generate an answer that is incorrect. Usually, such errors are due to simple mistakes (e.g., poor arithmetic). To solve this problem, recent research has explored a programatic approach that encourages the LLM to generate chains of thought with both natural language and code components. Then, the LLM can run this code via an external interpreter to obtain needed outputs. <a href="https://towardsdatascience.com/program-aided-language-models-93d226c7d9a0">Click Here</a>