Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought | by Salvatore Raieli | Aug, 2024

|LLM|PROMPT ENGINEERING|COT|REASONING|

Sometimes few words are enough: reducing output length for increasing accuracy

Brevity is a great charm of eloquence. — Marcus Tullius Cicero

Brevity and conciseness are the parents of correction. — Hosea Ballou

Large language models (LLMs) have shown interesting capabilities in the field of reasoning. With their use, a new field of application has emerged: prompt engineering. In fact, interaction with these models occurs through the use of prompts, and for this reason, techniques have been developed to improve these capabilities of LLMs.

One of the most intriguing techniques is chain-of-thought (CoT) prompting; this technique increases correctness in reasoning problems and explains how the model arrives at the solution (or what reasoning errors it…