AI Researchers at Princeton and Google Propose ReAct: A Powerful AI Method for Synchronizing Reasoning and Acting in Long Language Models

Although Extended Language Models (LLMs) have shown surprising performance on tasks involving interactive decision making and language comprehension, their reasoning capabilities (such as chain of thought) and acting ( such as generating action plans) have been studied mainly as separate topics. Recent work focuses on translating text contexts into text actions using the internal knowledge of the language model by using pre-trained language models to act in various interactive environments (such as text games, online browsing, etc.). In contrast, with thought chain prompting, a model generates reasoning using its internal representations and is not anchored in the outside world. This restricts your ability to investigate, reason, or update your knowledge in response to facts.

In their most recent project, a research team at Google Research investigated the use of LLMs to produce embedded reasoning traces and task-specific actions. The researchers presented a generic paradigm in their research paper titled “ReAct: Synergizing Reasoning and Acting in Language Models” to enable language models to handle a variety of language reasoning and decision-making tasks. They show that the Reason+Act (ReAct) paradigm consistently performs better than both reason-only and act-only paradigms when it comes to inducing larger language models, optimizing smaller language models, and improving human interpretation and reliability. ReAct makes it possible for language models to produce verbal reasoning traces and text actions at the same time.

The PaLM-540B frozen language model used in ReAct prompt configuration is requested with a limited number of in-context examples to produce task-solving domain-specific actions (such as “find” in answer questions and “go to ” in the living room). navigation). When performing tasks where reasoning is crucial, the creation of reasoning and action traces alternates, resulting in a task-solution trajectory that includes several reasoning-action-observation phases. Rather, reasoning traces need only be sparsely present at the most crucial locations of a path in decision-making tasks that may involve a large number of actions. In this case, the hints are written using sparse reasoning, and the language model determines when the actions and reasoning traces will occur asynchronously. The group also investigated the use of ReAct format trajectories to optimize smaller language models. The ReAct-powered PaLM-540B model was used to generate trajectories. The task success trajectories were then used to fit smaller language models (PaLM-8/62B) to reduce the need for extensive human annotation.

For assessment purposes, four benchmarks: Question Answering (HotPotQA), Fact Checking (Fever), Text-Based Gaming (ALFWorld), and Web Page Browsing (WebShop) were used to compare ReAct and the lines state-of-the-art foundation. . When it comes to answering questions (HotpotQA) and fact checking (Fever), the model overcomes the common problems of hallucinations and error propagation in thought chain reasoning by interacting with a simple Wikipedia API and producing problem solving paths. human-like tasks that are more interpretable than baselines without traces of reasoning. Additionally, ReAct outperforms imitation and reinforcement learning techniques in two interactive decision-making benchmarks, ALFWorld and WebShop, while only being given one or two examples in context, with absolute success rates of 34% and the 10%, respectively.

The research team also investigated human in-circuit interactions with the system by giving a human inspector control over ReAct’s reasoning traces. ReAct was shown to be able to alter its behavior to meet inspector reviews and perform a task effectively simply by replacing a mind-bending line with inspector hints. ReAct greatly simplifies troubleshooting because it only requires manual editing of a small number of ideas, opening up new possibilities for human-machine collaboration.

ReAct is a simple but successful technique for integrating acting and thinking into language models, to put it briefly. It shows that it is possible to describe thought, behavior and environmental feedback within a language model, resulting in a flexible agent that can handle problems that require interacting with the environment. ReAct achieves enhanced performance with understandable decision traces through various experiments that focus on interactive decision challenges, fact checking, and multi-hop question answering. Google intends to continue working on ReAct using the tremendous potential of the language model to tackle more complex built-in tasks. They want to achieve this by using techniques like large multitask training and pairing ReAct with powerful reward models.


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Khushboo Gupta is a consulting intern at MarktechPost. He is currently pursuing his B.Tech at the Indian Institute of Technology (IIT), Goa. She is passionate about the fields of machine learning, natural language processing, and web development. She likes to learn more about the technical field by participating in various challenges.


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