Awọn ohun elo Itọsọna

Ilana Aṣoju ReAct

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

Lori iwe yi1 iseju kika
  1. Akopọ
  2. Jin Dive
  3. Real-World imuse
  4. Ojo iwaju ti Ilana Aṣoju ReAct
  5. Fifi o sinu iwa
  6. Tesiwaju Ṣiṣawari
  7. Awọn ibeere ti a beere nigbagbogbo

Akopọ

The original paper evaluates it on knowledge-intensive question answering and interactive decision-making tasks.

Jin Dive

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.

Real-World imuse

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.

Ojo iwaju ti Ilana Aṣoju ReAct

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.

Fifi o sinu iwa

Kini idi ti o ṣe pataki

  • Kọ awọn yiyan. Apẹrẹ ipele-ohun elo pinnu boya AI ṣe ilọsiwaju awọn abajade gidi.
  • Ẹgbẹ ati ṣiṣan iṣẹ. Ijọpọ iṣan-iṣẹ ti o dara ṣẹda awọn anfani iṣẹ-ṣiṣe ti awọn olumulo le gbẹkẹle.
  • Ewu ati ailewu. Awọn ọran lilo ti iwọn daradara dinku rirẹ iyipada ati eewu imuse.

Ṣọra fun

  • Ṣiṣẹda ilana fifọ le ṣe alekun awọn iṣoro to wa tẹlẹ.
  • Awọn ẹgbẹ le ṣe adaṣe adaṣe ki o yọ idajọ eniyan ti o nilo kuro.
  • Didara le fò ti awọn abajade ko ba ni iṣiro nigbagbogbo.

Next awọn igbesẹ

  1. Ṣe maapu iṣan-iṣẹ lọwọlọwọ ki o ṣe idanimọ igbesẹ ti o ga julọ.
  2. Ṣe alaye awọn aaye ayẹwo eniyan ṣaaju adaṣe ni kikun.
  3. Kọ awọn olumulo lori awọn itọsi, awọn ọna igbega, ati awọn iṣedede didara.
  4. Tọpinpin awọn abajade ipele-ṣiṣe lati jẹrisi iye idaduro.

Tesiwaju Ṣiṣawari

Ìwé ìròyìn ọfẹ

Gba ìkànìyàn AI ojoojúmọ́

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

Take the ReAct Agent Pattern quiz

Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.

Bẹrẹ adanwo

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

Awọn ibeere ti a beere nigbagbogbo

Kini Ilana Aṣoju ReAct?

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.

Awọn agbara meji wo ni ilana ReAct darapọ?

ReAct duro fun Idi ati Ṣiṣe; o interleaves pq-ti-ero ero pẹlu nja sise bi ọpa awọn ipe.

Kini awọn eroja atunwi mẹta ti lupu ReAct?

Awọn iyipo loop ReAct nipasẹ Ero kan (ero), Iṣe kan (ipe irinṣẹ), ati akiyesi (abajade), tun ṣe titi o fi ṣe.

Kini 'Akiyesi' ni loop ReAct?

Akiyesi ni esi ti eto naa yoo pada lẹhin ṣiṣe iṣẹ ti o yan awoṣe, eyiti awoṣe lẹhinna ṣe idi.

Kini idi ti ReAct le dinku hallucination ti a fiwera pẹlu ero-ero mimọ?

Nipa ifọrọwerọ pẹlu awọn akiyesi gidi lati awọn irinṣẹ, awoṣe le ṣayẹwo awọn ododo ati ilana ti o tọ dipo kiko awọn idahun.

What improvement did the ReAct paper report over imitation/RL baselines on ALFWorld?

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.