Up tókànItọsọna atẹle
Awọn Aṣoju Oluṣeto-Alase
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ReAct is a language-model prompting pattern that interleaves reasoning traces with actions and observations from an external tool or environment.
Gbọ si itọsọna yiiAI-ṣe afihan · ~1 min
The original paper evaluates it on knowledge-intensive question answering and interactive decision-making tasks.
The ReAct paper prompts language models to interleave reasoning traces and task-specific actions, using observations from an external environment to update plans. It evaluates HotpotQA and FEVER with a Wikipedia API and interactive decision benchmarks including ALFWorld and WebShop. The authors report a 34-point absolute success-rate improvement on ALFWorld and 10 points on WebShop over the baselines used in their experiments. These findings are specific to the tested tasks and prompts.
Representing a trajectory as Thought, Action, Observation and a final answer.
Using the paper’s Wikipedia API to retrieve evidence for HotpotQA and FEVER questions.
Updating a plan after an environment returns an observation.
Comparing the original paper’s ReAct results with reasoning-only and acting-only baselines in its tested tasks.
Evaluate ReAct-style loops on the specific environment, tool reliability and task distribution. Include access limits, error handling and stopping conditions; the paper’s benchmark results do not guarantee safety or accuracy in unrelated deployments.
Ìwé ìròyìn ọfẹ
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
ReAct is a language-model prompting pattern that interleaves reasoning traces with actions and observations from an external tool or environment. The original paper evaluates it on knowledge-intensive question answering and interactive decision-making tasks.
ReAct duro fun Idi ati Ṣiṣe; o interleaves pq-ti-ero ero pẹlu nja sise bi ọpa awọn ipe.
Awọn iyipo loop ReAct nipasẹ Ero kan (ero), Iṣe kan (ipe irinṣẹ), ati akiyesi (abajade), tun ṣe titi o fi ṣe.
Akiyesi ni esi ti eto naa yoo pada lẹhin ṣiṣe iṣẹ ti o yan awoṣe, eyiti awoṣe lẹhinna ṣe idi.
Nipa ifọrọwerọ pẹlu awọn akiyesi gidi lati awọn irinṣẹ, awoṣe le ṣayẹwo awọn ododo ati ilana ti o tọ dipo kiko awọn idahun.
The original ReAct paper reports an absolute success-rate improvement of 34 points over imitation and reinforcement-learning baselines on ALFWorld, with a separate 10-point result on WebShop; this is scoped to its experiments.
Tesiwaju kikọ
Awọn itọsọna diẹ sii ti a yan fun koko yii
Up tókànItọsọna atẹle
Awọn Aṣoju Oluṣeto-Alase
Awọn ohun elo