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Decomposed Prompting: A Modular Approach for Solving Complex Tasks

ICLR 2023 · Large language models › Agents, code and math · approach: LLM-guided program synthesis · 99 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace regex answer extraction after CoT with a GPT3 answer-extraction sub-task handlerCoT math reasoning · MultiArithaccuracy: +17 pts (78 to 95)Helped
replace regex answer extraction after CoT with a GPT3 answer-extraction sub-task handlerCoT math reasoning · GSM8Kaccuracy: +14 pts (36 to 50.6)Helped
decompose letter concatenation into split, str_pos, merge sub-tasks instead of a single CoTk-th letter concatenation · Letter concatenation (k=3, space delimiter, longer inputs)exact matchHelped
recursive decomposition of list reversal (split halves) vs base CoTList reversal with davinci-001 · Reversal, 4-10 itemsexact matchHelped
unrolling the decomposition into a single CoT (rolled-out) for reversal (ablation)List reversal · Reversal, longer listsexact matchHurt
fine-grained vs coarse-grained decomposition (more question-type sub-tasks) on CommaQA (ablation)CommaQA-E · CommaQA-Eexact matchHelped
decomposition into sub-task handlers (vs CoT) on CommaQA-ECommaQA-E · CommaQA-E (coarse and fine decomposition)exact matchHelped
retrieval via Elasticsearch-based retrieve_odqa decomposition vs NoDecomp retrieval baseline (Flan-T5-XXL)Open-domain multihop QA · 2WikiMultihopQA, MuSiQue, HotpotQAanswer F1Helped
Elasticsearch-based decomposed retrieval with Codex vs NoDecomp retrieval on HotpotQAOpen-domain multihop QA (Codex) · HotpotQAanswer F1No clear effect
decomposition with differently-structured sub-question generation (GPT3 generates each question instead of looping) for letter concatenation (ablation)k-th letter concatenation · Letter concatenationexact matchNo clear effect
tail-recursive reversal (reverse(list[1:]) + head) instead of halving (ablation)List reversal · Reversal, longer inputsexact matchMixed

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