Why can the same big model sometimes provide quick and incorrect answers, but sometimes it can reliably deduce results? The difference often lies not in the model itself, but in 'how to ask'. The Chain of Thought (CoT) prompt is one of the most representative methods among them.
1、 What is a thought chain
The core of the thought chain is simple: guiding the model to output reasoning steps first in prompts, and then giving the final answer, rather than jumping directly to the conclusion. Researchers have found that when models are asked to "think step by step," their accuracy in tasks such as mathematics, logic, and common sense reasoning significantly improves.
2、 Why is it effective
The big model essentially predicts the next word based on the previous context. Directly providing the answer, the model must 'compress' the entire inference process in a forward calculation; Writing out the intermediate steps gives the model more "thinking space", breaking down complex problems into several simple ones, making the context of each step clearer and errors easier to detect.
3、 Several common forms
1. Small sample thinking chain: Provide several examples of "problem reasoning answer" in the prompt, and the model will imitate them.
2. Zero sample thinking chain: Just add the sentence 'let's think step by step' without providing examples.
3. Self consistency: Let the model sample different inference paths multiple times, then vote on the answers and take the majority result.
4. Mind tree/mind map: Extend linear reasoning into tree or graph like search, allowing branching, backtracking, and evaluation.
4、 When to use and when not to use
The thought chain is suitable for tasks that require multi-step reasoning: arithmetic, logic, code, and planning. But for simple retrieval based question answering, it may increase latency and cost, and even introduce unnecessary errors. It also has a side effect that needs to be noted: the inference process written by the model "looks" reasonable, but it may not be the true calculation path inside it, so it cannot be considered a reliable explanation.
5、 The Evolution of Large Model Capability
With the increase in model size and the introduction of reinforcement learning training, "reasoning before answering" has gradually evolved from a prompting technique to an internalization ability of the model. The new generation of inference models will actively generate longer thought processes, and as a result, the thought chain will shift from "cue engineering" to "training objectives".
Summary in one sentence: The thought chain allows big models to write down the process of "thinking" and exchange more tokens for more stable reasoning - understanding its principles and boundaries is more important than remembering the phrase "let's think step by step".
[Reference source] Comprehensive compilation of industry information publicly released (such as public papers on Chain of Thoght Prompting, Self Consistency, etc.).