Multimodal Chain-of-Thought Reasoning in Language Models

Author

• Harshitha Thoram (ORCID: )

Introduction

What Is Chain-of-Thought Prompting?

Imagine solving a math problem or logic puzzle by writing out each step of your thinking process. Large language models (LLMs) can do something similar! Chain-of-thought (CoT) prompting is a technique where we encourage the model to generate intermediate reasoning steps before finalizing an answer​

In other words, the model “thinks out loud” — it might list facts, perform calculations, or logically work through the question step-by-step, and then use that reasoning to produce the answer. This approach often leads to better accuracy on complex problems because the model isn’t trying to jump directly to the answer; it’s iteratively working it out, much like we would.

CoT prompting can be done explicitly by adding cues in the prompt. For example, you might append a phrase like “Let’s think step by step” to your question. This signals the model to spill its thought process. Researchers have found that with the right prompts or fine-tuning, even very large models can produce impressive multi-step reasoning — solving math word problems, answering tricky commonsense questions, and more — all by…