Few-shot and Chain-of-Thought: Raising Reasoning Accuracy

Few-shot Prompting

A technique where showing 3-5 examples of "for this kind of question, answer this way" makes the LLM answer in the same manner. Zero examples is Zero-shot, one is One-shot.

Example: Sentiment Classification

Classify the following reviews as "pos / neg / neutral":

Review: "The wait was long and tiring"
Class: neg

Review: "The food was good but the price is so-so"
Class: neutral

Review: "The staff was kind and it was comfortable"
Class: pos

Review: "[target]"
Class:

How You Pick Examples Is Key

• Diversity: not only similar examples; include different patterns
• Boundary cases: include one hard-to-judge intermediate example
• Same format: unify output format in examples and production
• Order effect: the last example influences the result more

Cases Where Few-shot Works

• Unique output formats (CSV, special JSON)
• Company-specific tone
• Use of jargon
• Examples including "this way of answering is bad"

Chain-of-Thought (CoT)