How Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) Create Retrieval-Augmented-Thought(RAT)?

Imagine an AI assistant that can write like Shakespeare and reason like an expert. It sounds impressive, right? However, what if this assistant sometimes struggles with factual accuracy, relying on outdated information or simply making things up? Retrieval-Augmented Thoughts (RAT) is a revolutionary approach that combines two key techniques: Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) prompting. Large Language Models (LLMs) have become popular for their ability to mimic human-like writing and provide fluent answers to questions. But sometimes, their responses need to be grounded in real-world knowledge. RAT addresses this issue by providing a revolutionary approach to AI reasoning. Let’s delve deeper and understand the code of RAT!

Alright, alright, hold on a sec before we dive into the deep end. Let’s break down this whole prompt thing first. Imagine you have this super cool AI assistant, like a fancy digital genie. A prompt is the magic spell you use to tell it what you want. You can ask it to write you a story, translate a language, or answer a question in a super informative way. It’s all about giving the AI clear instructions.

Prompt engineering is like taking those instructions and turning them into a Michelin-star recipe for the AI. You can get the AI to cook up some seriously impressive results by tweaking the prompt just right. It’s about ensuring the AI understands exactly what you need and delivers the best possible response.

Here’s the cool part: prompt engineering lets you unlock the full potential of the AI. You can use it for all sorts of things, from writing killer poems to tackling super complex problems. Plus, there are even some advanced techniques like One-Shot, Zero-Shot, Few-Shot, Chain-of-Thought, Instructional, and Iterative prompts, each serving a different purpose for uncomplicated tasks to complex, multi-step processes.

Now, let’s talk about RAT, a novel approach that combines two powerful techniques: Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT). Let’s explore how this duo elevates AI reasoning to new heights.

Retrieval-Augmented Generation (RAG): The Knowledge Infuser

Imagine an LLM working on a math problem. RAG acts like a helpful tutor. It allows the LLM to access relevant information from external sources, like formulas or theorems, during the reasoning process. This ensures the LLM’s steps are grounded in factual knowledge, reducing the chances of fantastical solutions.

“If you want to learn more about RAG, please refer to my previous post.”

What is RAG(Retrieval-Augmented Generation)?

How does Retrieval-Augmented Generation (RAG) Work?

Chain-of-Thought (CoT): Making Thinking Visible

Chain-of-Thought Prompting: Helping Large Language Models Show Their Work

Large language models (LLMs) are great at generating text, but they can struggle with complex problems that require step-by-step reasoning, like solving word problems.

CoT prompting tackles this by encouraging LLMs to explain their thinking. Instead of just giving a final answer, the LLM shows its “work” by breaking the problem down into smaller steps. This is like showing your calculations in math class.

There are two ways to get LLMs to use CoT prompting:

• Zero-shot prompting: We use special words or phrases in the prompt itself, like “Let’s think step by step,” to nudge the LLM into explaining its reasoning.
• Few-shot prompting: We show the LLM a few examples of how to solve similar problems, where the solution steps are explained clearly.

However, there are some challenges with CoT prompting.

• LLMs might make mistakes: If they don’t have enough knowledge about the topic, their reasoning steps might be wrong.
• LLMs might get stuck on false ideas: Sometimes, they might come up with their own explanations that aren’t based on reality.

Figure: Chain-of-thought prompting enables large language models to tackle complex arithmetic, commonsense, and symbolic reasoning tasks. Chain-of-thought reasoning processes are highlighted.

Few-shot prompting gives a few examples to help the language model understand what it should do, while Chain-of-Thought prompting shows step-by-step reasoning from start to finish. This helps with complex tasks that require symbolic reasoning and intermediate steps. It works best with larger models, while smaller models may create odd thought chains and be less precise. In some cases, you can use Zero-Shot CoT prompting without showing intermediate steps.

RAT: The Marriage of Knowledge and Transparency

Retrieval Augmented Thoughts (RAT) is a simple but effective prompting approach that combines Chain-of-Thought (CoT) prompting with retrieval augmented generation (RAG) to handle long-term reasoning and generation issues.

As a result, LLMs generate zero-shot chain-of-thoughts (CoT), which are merged with RAG. Using the ideas as inquiries, causally amending them and gradually developing the response.

Iteratively revising a chain of thoughts using information retrieval significantly enhances the reasoning and generation abilities of large language models when dealing with long-horizon generation tasks. This approach also greatly reduces the occurrence of hallucinations. The proposed method called retrieval-augmented thoughts (RAT), involves revising each thought step one by one with information retrieved from relevant sources. This includes the task query, as well as the current and past thought steps after the initial zero-shot CoT is generated.

By applying RAT to various base models, significant improvements have been observed in their performances on various long-horizon generation tasks. On average, there has been a relatively increasing rating score of 13.63% for code generation, 16.96% for mathematical reasoning, 19.2% for creative writing, and 42.78% for embodied task planning.

Figure 1 | Pipeline of Retrieval Augmented Thoughts (RAT). Given a task prompt (denoted as I in the figure), RAT starts from initial step-by-step thoughts (𝑇1, 𝑇2, · · ·, 𝑇𝑛) produced by an LLM in zero-shot (“let’s think step by step”). Some thought steps (such as 𝑇1 in the figure) may be flawed due to hallucination. RAT iteratively revises each thought step using RAG from an external knowledge base (denoted as a Library).

The diagram outlines the Retrieval Augmented Thoughts (RAT) process, a method for prompting large language models (LLMs) to improve their reasoning abilities in long-horizon tasks. Here’s a breakdown of the key elements:

Step 0: Initial Draft

• A task prompt is presented to the LLM.
• The example shows a prompt about obtaining diamonds in Minecraft.

Step 1-Step n: Iterative Refinement

• The LLM generates an initial response based on its understanding of the prompt (zero-shot CoT). This might be flawed due to a lack of specific information.
• RAT incorporates CoT prompting, where the LLM iteratively revises its response by explaining its reasoning for each step (Ti).

Key Components

• Task Prompt: This is the starting point, providing the LLM with the question or problem to solve.
• LLM: This represents the large language model itself.
• Initial CoTs (Ti-1, Ti): These are the LLM’s initial and revised thought chains during the iterative process.
• Library: This symbolizes the external knowledge base that the LLM can access through Retrieval-Augmented Generation (RAG).
• Augmented Revision: This refers to how the LLM refines its thought chains (Ti) based on the retrieved information and previous explanations.

The RAT Process

1. Initial Response: The LLM generates an initial response based on the prompt (T0).
2. Explanation: The LLM explains its reasoning behind the initial response (T1–1).
3. Retrieval: RAT retrieves relevant information from the external knowledge base (Library) based on the explanation.
4. Revision: The LLM revises its thought chain (T1) by incorporating the retrieved information.
5. Repeat: Steps 2–4 are repeated iteratively until the LLM arrives at a satisfactory solution (Tn).

Overall, the image source from the research paper and highlights how RAT can address the limitations of LLMs in complex reasoning tasks by incorporating external knowledge retrieval and step-by-step explanation.

Figure | Top: An example of different LLM reasoning methods on creative generation tasks. Red text indicates errors or illusions in the text generated by LLM, while green text represents correct generation. Methods without RAG often generate incorrect information with hallucination, classical RAG is highly related to retrieved content with a loose structure, and RAT-generated texts perform best in terms of accuracy and completeness. Bottom: The quantitative performance comparison for different LLM reasoning methods on complex embodied planning, mathematical reasoning, code generation, and creative generation tasks. Our RAT outperforms all the baselines on all tasks.

Benefits of RAT

• Improved Accuracy: By allowing the LLM to access external knowledge and refine its reasoning, RAT helps to reduce errors and generate more accurate solutions.
• Enhanced Explainability: The iterative process with explanations provides insights into the LLM’s thought process, making it easier to identify and address any issues.
• Stronger Long-Horizon Reasoning: RAT is particularly beneficial for complex tasks requiring multiple steps, where reasoning transparency is crucial.

The Retrieval Augmented Thoughts (RAT) method can be summarized in the following points:

1. It helps to maintain factual accuracy over extended reasoning tasks, which is a gap in LLMs’ ability.
2. It reduces hallucinations by revising each reasoning step with relevant retrieved information, ensuring contextually aware outputs.
3. It is versatile and can be applied across various tasks such as code generation, mathematical reasoning, creative writing, and task planning.
4. It sets new benchmarks for the performance, accuracy, and reliability of LLM outputs, paving the way for future advancements in AI reasoning capabilities.

The Future Landscape: Where RAT Can Lead Us

RAT represents a significant leap forward in LLM reasoning capabilities. Here’s a glimpse into the exciting possibilities it unlocks:

• Personalized Learning: Imagine LLMs equipped with RAT becoming intelligent tutors, explaining concepts step-by-step and adapting their explanations based on the student’s understanding. This personalized approach has the potential to revolutionize education.
• Scientific Discovery Acceleration: LLMs empowered by RAT can collaborate with scientists, proposing hypotheses and reasoning through experiments, potentially accelerating the pace of scientific discovery.
• Explainable AI for Higher Trust: RAT paves the way for Explainable AI (XAI), where LLMs can not only generate solutions but also explain their thought processes. This transparency fosters trust and collaboration between humans and machines.

Challenges and Considerations for RAT:

• Information Overload: Efficient retrieval and processing of vast amounts of information from diverse external sources will be crucial for handling complex reasoning tasks.
• Automatic Chain Generation: Currently, CoT prompts often require manual intervention. Developing algorithms for generating CoT explanations automatically will streamline the process and make RAT more scalable.
• Ethical Considerations: As LLMs become more adept at reasoning, ethical concerns regarding bias and fairness become paramount. Research into mitigating bias and ensuring the responsible development of RAT will be essential.

Conclusion: Ushering in a New Era of Explainable Reasoning

Retrieval-augmented thought signifies a paradigm shift in the way LLMs approach complex tasks. 🤖💭🚀 RAT opens doors to a future of powerful and explainable AI systems by fostering transparency and grounding reasoning in factual knowledge. 🌟🔍 As research progresses, we can expect even more sophisticated techniques that leverage external knowledge, transparent reasoning, and humans. 🧑‍🔬🌐🤝.

References

• https://arxiv.org/pdf/2403.05313
• https://www.promptingguide.ai/