P-CoT prompting improves many LLM results on PhonologyBench tasks, but it does not consistently beat baselines across all models and tasks as the paper claims.
Prompting Large Language Models With the Socratic Method
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abstract
This paper presents a systematic approach to using the Socratic method in developing prompt templates that effectively interact with large language models, including GPT-3. Various methods are examined, and those that yield precise answers and justifications while fostering creativity and imagination to enhance creative writing are identified. Techniques such as {\em definition}, {\em elenchus}, {\em dialectic}, {\em maieutics}, {\em generalization}, and {\em counterfactual reasoning} are discussed for their application in engineering prompt templates and their connections to inductive, deductive, and abductive reasoning. Through examples, the effectiveness of these dialogue and reasoning methods is demonstrated. An interesting observation is made that when the task's goal and user intent are conveyed to GPT-3 via ChatGPT before the start of a dialogue, the large language model seems to connect to the external context expressed in the intent and perform more effectively.
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cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
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P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs
P-CoT prompting improves many LLM results on PhonologyBench tasks, but it does not consistently beat baselines across all models and tasks as the paper claims.