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Plan-and-Solve prompting improves LLM reasoning by first devising a plan

Researchers have developed a new prompting technique called Plan-and-Solve (PS) to improve the reasoning capabilities of large language models. PS prompts the model to first create a plan of subtasks before executing them, which helps prevent errors like missing steps or incorrect arithmetic. An enhanced version, PS+, further refines this by instructing the model to extract relevant variables and pay close attention to calculations and common sense, significantly improving accuracy on complex word problems. AI

IMPACT This technique could improve the reliability of LLMs for complex reasoning tasks, reducing errors in calculations and planning.

RANK_REASON The item describes a new prompting technique for LLMs, which is a research contribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Plan-and-Solve prompting improves LLM reasoning by first devising a plan

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The item describes a new prompting technique for LLMs, which is a research contribution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Devanshu Biswas ·

    Plan-and-Solve: make the model plan the steps before it computes any of them

    <p>Ask a plain language model a multi-step word problem and it will often blurt out a confident number that is wrong — not because the model is dumb, but because it quietly skipped a step or fumbled the arithmetic. "Let's think step by step" (zero-shot chain-of-thought) helps a l…