Retrieval Augmented Thoughts (RAT): An AI Prompting Strategy that Synergies Chain of Thought (CoT) Prompting and Retrieval Augmented Generation (RAG) to Address the Challenging Long-Horizon Reasoning and Generation Tasks

The quest for models that can think, reason, and generate outputs similar to a human’s capacity for complex problem-solving has been paramount. Large language models (LLMs) are at the forefront, designed to mimic human-like understanding and articulation of ideas. Despite remarkable achievements, these models often grapple with the challenge of maintaining factual accuracy over extended reasoning tasks, leading to what is known as hallucinations – generating plausible but factually incorrect information. This phenomenon is particularly pronounced in scenarios requiring a series of logical steps, highlighting a gap in the LLMs’ ability to reason with precision and context awareness over longer horizons.

The endeavor to bridge this gap has led researchers to propose various methodologies aiming to refine the reasoning process of LLMs. Earlier approaches have explored the integration of external information retrieval with model-generated content, attempting to anchor the models’ outputs in factual accuracy. However, these methods typically fall short in dynamically refining the reasoning process, often producing results that, while improved, still need to improve the desired level of contextual understanding and accuracy.

Researchers from Peking University, the University of California Los Angeles, and the Beijing Institute for General Artificial Intelligence proposed the Retrieval Augmented Thoughts (RAT) method directly responds to maintaining factual accuracy in LLMs. RAT is a novel approach emphasizing the iterative revision of the model’s generated thoughts. RAT effectively mitigates the issue of hallucinations by harnessing external information relevant not just to the initial query but also to the evolving context of the model’s reasoning process. This is achieved by revising each step of the model’s generated chain of thoughts with pertinent information retrieved from vast databases, ensuring that each reasoning step is grounded in accuracy and relevance.

The RAT method’s versatility excels across long-horizon generation tasks, from generating complex code to solving intricate mathematical problems, crafting creative narratives, and planning functions in simulated environments. RAT consistently enhances the performance of LLMs, which is quantified in significant performance improvements. For instance, it has led to an average increase of 13.63% in rating scores for code generation tasks and marked enhancements in mathematical reasoning with a 16.96% increase in rating scores, 19.2% in creative writing rating scores, and a significant 42.78% in embodied task planning tasks. These achievements underscore RAT’s efficacy and its potential as a universally applicable solution for enhancing LLM reasoning capabilities.

RAT’s implementation reveals the potential for LLMs to achieve a more human-like ability to reason and generate responses. By iteratively refining the thought process with contextually relevant information, the method advances the frontier of what LLMs can achieve, setting new standards for accuracy, reliability, and context awareness in AI-generated content.

In conclusion, the Retrieval Augmented Thoughts (RAT) method can be presented in the following points:

• Bridges the gap in LLMs’ ability to maintain factual accuracy over extended reasoning tasks.
• Mitigates hallucinations by revising each reasoning step with pertinent, retrieved information, ensuring contextually aware outputs.
• Demonstrates versatility across various tasks, including code generation, mathematical reasoning, creative writing, and task planning, showcasing universal applicability.
• Sets new benchmarks for the performance, accuracy, and reliability of LLM outputs, paving the way for future advancements in AI reasoning capabilities. 

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