Dyck languages involve formal tasks such as completing sequences of nested brackets or parentheses to ensure syntactic validity. Chain-of-Thought (CoT) prompting assists language models in these symbolic reasoning tasks by encouraging step-by-step state tracking. For example, given the input sequence `[ {`, the model uses CoT to explicitly track the stack's state at each operation (e.g., `0: empty stack`, `1: [ ; stack: [`, `2: { ; stack: [ {`) to systematically determine that the correct closing sequence is `} ]`.

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Chain-of-Thought (CoT) methods are highly versatile and can be applied to a wide variety of tasks beyond simple algebraic calculations. Typical problem-solving scenarios where CoT prompting is effectively used include mathematical reasoning, logical reasoning, commonsense reasoning, symbolic reasoning, and code generation.

Problem-Solving Scenarios for Chain-of-Thought Prompting

Reference of Foundations of Large Language Models Course

In CommonsenseQA (CSQA) tasks, Chain-of-Thought (CoT) prompting helps language models utilize general, everyday knowledge for reasoning. For example, when answering "What type of person typically contracts illness?", the CoT approach guides the model to explicitly reason that the correct choice must be someone susceptible to illness. It then deduces that an "elderly person" is the correct answer because they typically have weaker immune systems, thereby linking common sense to the final conclusion through intermediate steps.

Application of CoT to CommonsenseQA (CSQA)

StrategyQA tasks evaluate a language model's ability to perform multi-hop logical deductions based on implicit facts. Chain-of-Thought (CoT) prompting facilitates this by breaking down complex questions into explicit, logical steps. For instance, when evaluating whether hamsters provide food for any animals, the CoT reasoning process explicitly articulates the premises that hamsters are prey animals and that prey are food for predators, which logically leads to the correct affirmative conclusion.

Application of CoT to StrategyQA

Application of CoT to Dyck Languages

Last Letter Concatenation is a symbolic string manipulation task where the final characters of each word in a phrase are extracted and joined sequentially. Chain-of-Thought (CoT) prompting improves performance on this task by having the language model process one word at a time. For the phrase "Elon Musk", the CoT method explicitly isolates the last letter of "Elon" as "n", identifies the last letter of "Musk" as "k", and then concatenates them to produce the correct final string "nk".

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