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ReAct: Synergizing Reasoning and Acting in Language Models

ICLR 2023 · Large language models › Agents, code and math · approach: Agent instruction-following benchmarks

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Interleave reasoning thoughts with actions and external observationsPaLM-540B · HotpotQAEM: 25.7 -> 27.4Helped
Interleave reasoning thoughts with actions and external observationsPaLM-540B · Feveraccuracy: 58.9 -> 60.9Helped
Interleave sparse reasoning thoughts with actionsPaLM-540B · ALFWorldsuccess rate (All, best trial): 45 -> 71Helped
Interleave reasoning thoughts with actionsPaLM-540B · WebShopsuccess rate: 30.1 -> 40.0Helped
Combine ReAct with CoT-SC, backing off from ReAct when it cannot answer within the step limitPaLM-540B · HotpotQAEM: 33.4 -> 35.1Helped
Combine CoT-SC with ReAct when the sampled answers lack consensusPaLM-540B · Feveraccuracy: 60.4 -> 64.6Helped
Add reasoning thoughts to action-only trajectories (ablation)ReAct · HotpotQAEM: 25.7 -> 27.4Helped
Add reasoning thoughts to action-only trajectories (ablation)ReAct · Feveraccuracy: 58.9 -> 60.9Helped
Add sparse reasoning thoughts to action-only trajectories (ablation)ReAct · ALFWorldsuccess rate (All, best trial): 45 -> 71Helped
Add reasoning thoughts to action-only trajectories (ablation)ReAct · WebShopsuccess rate: 30.1 -> 40.0Helped
Replace flexible sparse reasoning with dense external-feedback thoughts (ablation)ReAct on ALFWorld · ALFWorldsuccess rate (All, best trial): 71 -> 53Hurt
Use GPT-3 (text-davinci-002) instead of PaLM-540BReAct · HotpotQA validation (500-question subset)exact match: 29.4 -> 30.8Helped

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