LLM’s, RAG, SFT, CoT, CoT-SC, ToT and Their Combinations

In this post, to compare the capabilities of Large Language Models (LLM’s) when used alone versus when enhanced with Retrieval Augmented Generation (RAG), Supervised Fine-Tuning (SFT), or a combination of both. By blending traditional machine learning approaches with emerging techniques like Chain of Thoughts (CoT) and Tree of Thoughts (ToT), modern AI is evolving to tackle more complex reasoning, thought processes, and domain-specific challenges.

Before diving into the comparisons, let’s review some foundational concepts in machine learning:

Supervised Learning: Supervised learning is a training paradigm that relies on historical data with labels (input–output pairs). The objective is for the model to learn the mapping from input to output so that it can predict the correct outputs for new, unseen inputs.

?Unsupervised Learning: Unsupervised learning works with unlabeled data. The goal is to explore the given data and discover patterns, structures, or relationships without any guidance on what the outputs should be. Common tasks include clustering, dimensionality reduction, and anomaly detection, etc.

?Reinforcement Learning (RL): Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. Instead of being given correct answers, the agent learns from the consequences of its actions—through trial and error—using rewards (or penalties) to guide its improvements.

?Advanced Reasoning Techniques:

?Chain of Thoughts (CoT): Chain of Thoughts (CoT) is a prompt engineering technique that guides a language model to produce a series of intermediate reasoning steps before arriving at the final answer. It works by feeding clues and instructions to the model and then using these intermediary insights during problem solving. Essentially, CoT can be seen as the application of logical reasoning (like decision trees) to help the model break down complex queries.? It adds a layer of transparency to the model's reasoning process, making it easier to debug and understand.

In very high-level layman terms, CoT is like equipping a waterfall model (as seen in some RL approaches) with agile decision-making: it makes the model think “step-by-step” before giving a final answer. You can think of CoT as akin to combining Reinforcement Learning (RL) with Decision Trees (DT).

?Self-Consistency CoT (CoT-SC): Self-Consistency CoT (CoT-SC) builds on the basic CoT approach by asking the same question multiple times and deciding on the final answer through a majority vote. This is like employing an ensemble method like Random Forests (RF) to improve response reliability. That is, CoT-SC is roughly CoT combined with RF.

Tree of Thoughts (ToT): Tree of Thoughts (ToT) generalizes the idea of CoT by allowing the model to generate a tree of possible reasoning paths rather than a single linear chain. This method enables the model to:

·?????? Evaluate multiple reasoning paths.

·?????? Self-assess intermediate decisions.

·?????? Look ahead and backtrack when necessary to make a globally optimal choice.

ToT breaks down complex problems (which might involve math, logic, or intricate reasoning) into several sub-problems, using techniques inspired by breadth-first search (BFS) and depth-first search (DFS). Unlike CoT or CoT-SC where the solution follows a single linear path, ToT explores a “bag” of solutions, offering a range of ideas and possibilities at each decision point.

Reflecting on long-ago discussions with friends from college, I realize how comforting it is to see theoretical AI concepts come alive. The process of tracing search trees (using methods like AVL, RB, B-Trees, etc.) and learning algorithmic search techniques (BFS/DFS) brings back memories of our early days studying AI. Concepts from compiler design, such as NFA (Nondeterministic Finite Automaton) and DFA (Deterministic Finite Automaton), all tie into the modern practice of evaluating chains of thoughts.

Understanding Large Language Models (LLMs): A Large Language Model (LLM) is an AI model that uses Natural Language Processing (NLP) techniques to understand, generate, and process human language. Here are a few key points:

·?????? LLMs can “think through” a problem by predicting what word comes next in context.

·?????? They are trained on vast amounts of text data with billions of parameters.

·?????? Their ability to recognize patterns and context makes them effective at tasks like Question & Answer (QA), conversation, and natural language generation.

·?????? Strengths include being versatile with access to diverse, public data.

·?????? Challenges include being potentially outdated, struggling with domain-specific nuances, having information cutoffs, and sometimes producing hallucinated responses when faced with insufficient context.

Enhancing LLMs with RAG and SFT: Both RAG and SFT are techniques that further refine and specialize LLMs.

Retrieval Augmented Generation (RAG): RAG is an architecture that pairs a generative language model with a retrieval component. The process involves three main steps:

1.???? Retrieving (R):?Relevant chunks—whether pages, paragraphs, sentences, or words—are extracted from a corpus or data source.

2.???? Augmenting (A):?The retrieved information is combined with the original query to enrich the context.

3.???? Generating (G):?The augmented prompt is fed into the LLM to produce a more informed and contextually robust response.

·?????? A query is generated from the user prompt.

·?????? The query is converted into a vector and submitted to a vector database (VectorDB).

·?????? The top K relevant chunks are identified based on similarity using embeddings.

·?????? These chunks, along with the original query, are then processed by the LLM to produce a final answer.

Strengths of RAG:

·?????? It reduces typical LLM challenges by keeping the model updated with the latest domain-specific information.

·?????? It provides a defense against hallucinated responses by grounding outputs in reliable data.

Challenges of RAG:

·?????? Incorporating extra data increases the inference load.

·?????? Retrieval may need to be fine-tuned to handle highly specialized contexts.

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Supervised Fine-Tuning (SFT): Supervised Fine-Tuning adapts a pre-trained LLM to a specific task or domain using labeled datasets. The process involves:

·?????? Selecting a suitable base model.

·?????? Training it further on domain-specific labeled data.

·?????? Adjusting model parameters to reflect the unique characteristics of the dataset.

?Strengths of SFT:

·?????? It is excellent for specialized use cases that require deep insights into a specific domain’s language, history, and terminology.

·?????? SFT models usually provide faster inference and more consistent behavior within their niche.

?Challenges of SFT:

·?????? They share the same data cutoff limitations as base LLMs unless combined with retrieval methods like RAG.

·?????? They might underperform on general queries outside the specialized domain.

The Power of Combining SFT and RAG: LLMs that incorporate both SFT and RAG combine the best of both worlds:

·?????? SFT offers deep domain-specific expertise.

·?????? RAG continuously updates the model with the latest information.

This combination addresses both the need for up-to-date knowledge and the necessity of domain-specific accuracy. It is particularly powerful for environments that require:

·?????? Up-to-date information fused with domain-specific insights.

·?????? A reduced risk of hallucination via grounding in reliable data.

·?????? Rapid adaptation to new data while maintaining a strong foundational expertise.

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Comparative Analogies:

To better understand the differences between these approaches, consider the following analogies:

·?????? LLMs Alone:?Think of a standalone LLM as a large public library—comprehensive yet potentially outdated in some areas. They can provide immediate, generalized information, but might lack the specificity of a specialized archive.

·?????? LLMs with RAG:?These models are like live-in relationships; they continuously update their knowledge with current data, ensuring timely and informed responses, though sometimes they might miss deep contextual insights.

·?????? LLMs with SFT:?Similar to arranged marriages where both partners share a deep cultural or traditional bond, these models are fine-tuned on historical and domain-specific data, providing deep, contextual responses.

·?????? LLMs with SFT + RAG:?Imagine a partnership that combines the best of both worlds—familiarity with tradition and an awareness of current trends. This setup is ideal for business scenarios that require precision and up-to-date information.

·?????? LLMs with CoT:?These are like dependable friends or partners who help you reason through problems without overcomplicating the root issues.

·?????? LLMs with CoT-SC:?Similar to consulting multiple friends for a decision, this approach asks the same question several times, using majority voting to decide on the best answer.

·?????? LLMs with ToT:?Envision a partner with an academic background, well-versed in logical, mathematical, and technical reasoning. ToT works by producing multiple layers of reasoning, from which the best path is chosen.

Conclusion and Personal Note:

In summary, while traditional LLMs are highly versatile and powerful, enhancing them with RAG and/or SFT can dramatically improve their effectiveness:

·?????? RAG ensures the model stays updated by incorporating external data.

·?????? SFT specializes the model for domain-specific tasks.

·?????? The combination of SFT and RAG is particularly potent for applications requiring current, domain-aware responses.

·?????? CoT to be a refreshing approach because it mirrors how we naturally think, by breaking down large, complex problems into manageable steps.

·?????? CoT-SC resonates with me because it enforces a democratic form of reasoning within the model. Asking multiple times and collecting diverse answers before settling on a final decision is akin to seeking a second opinion. This redundancy not only boosts reliability but also provides a safety net against errors or overlooked details.

·?????? ToT stands out as it brings a whole new dimension to problem-solving. The ability to explore multiple branches of thought simultaneously is incredibly powerful. It’s like having several experts brainstorming different approaches, and then, by evaluating each option, converging on the best possible solution.

For me the way LLMs are growing up too fast with wide variety of techniques & scalability on NLP landscape, these techniques reflect the growing sophistication in AI reasoning. They bridge the gap between human decision-making and machine efficiency, making AI not just a repository of knowledge, but a partner in creative problem solving.

At this point, I personally lean towards a combination of SFT and RAG, as I find this setup best meets the need for up-to-date, contextually rich, and domain-specific interactions. This approach addresses many shortcomings of pure LLMs while delivering focused and reliable performance.

Food for Thought:? Close your eyes for a minute and ask yourself – Why is RAG so important? How did embeddings and vector databases become integral to modern LLMs?

RAG is critical because traditional LLMs have historically had limited memory—capable of processing only about six pages of text (roughly 4K tokens), which is minuscule compared to the extensive amount of data needed for deep domain-specific tasks. When you have hundreds or even thousands of pages of information or years of domain-specific manuals, a standard LLM simply cannot remember everything. That’s why we break up the data and store it in specialized formats like embeddings (using vector databases).

Using RAG, we can continuously update LLMs with external information, ensuring they remain relevant even when the original training data becomes outdated. However, carrying extra data comes with trade-offs in compute and storage costs. It’s essential to weigh these factors: are you ready to manage the additional computational load, or would a pure SFT approach better serve your needs? The answer depends on whether your business demands require high flexibility or low-latency responses on smaller, more static datasets.

Ultimately, the choice between pure LLMs, enhanced RAG, or specialized SFT, or and their combinations with advanced reasoning techniques like CoT or ToT— depends on your data organization, compute and storage capabilities, and the specific requirements of your use case. Whether we’re approaching an era of quantum computing or simply optimizing current technology, these trade-offs are key to advancing the capabilities of NLP systems.

Feel free to share your thoughts on these approaches. Let’s explore together how these techniques can be further refined to meet future business and technological challenges!

References: https://arxiv.org/pdf/2305.10601 & https://arxiv.org/pdf/2201.11903