Understanding Large Language Models and Their Retrieval Capabilities

Table of contents

1. Introduction to Large Language Models
2. The Structure of LLMs
3. Query Classification
4. Retrieval Techniques
5. Reranking and Repacking
6. Chunking and Embedding
7. Vector Database
8. Conclusion

In recent years, Large Language Models (LLMs) have made significant strides in natural language processing. These models can generate human-like text, perform translations, summarize information, and much more. This blog post will explore the components and functionalities of LLMs, focusing on their retrieval capabilities. We will break down complex concepts into simpler components, making it easier for beginners to grasp.

1. Introduction to Large Language Models

Large Language Models are advanced algorithms trained on vast amounts of text data to understand and generate human language. They form the backbone of many applications we use today, from chatbots to search engines.

Key Features of LLMs:

• Text Generation: LLMs can create coherent and contextually relevant text based on the input they receive.
• Context Understanding: They analyze the context of words to understand their meanings better, enabling more accurate responses.
• Flexibility: LLMs can be fine-tuned for specific tasks, such as summarization, question answering, and more.

2. The Structure of LLMs

A. Evaluation

Before deploying an LLM, it is crucial to evaluate its performance based on:

• General Performance: How well does the model perform in general tasks?
• Specific Domains: Is the model capable of understanding specialized jargon in certain fields?
• Retrieval Capability: How effectively can the model retrieve information based on queries?

B. Fine-tuning

To improve performance for specific applications, LLMs can undergo fine-tuning. This process adjusts the model based on:

• Disturb: Introducing variations to the training data.
• Random: Randomizing input data to enhance learning.
• Normal: Standard training processes without modifications.

3. Query Classification

When a user inputs a query, it must be classified effectively to retrieve relevant information. This process involves:

• Original Query: The user's direct input.
• BM25: A ranking function used for information retrieval that evaluates the relevance of documents.
• Contriever: A model designed to understand context and improve retrieval.
• LLM-Embedder: This component embeds queries into vector space for better matching against database entries.

Retrieval Techniques

Retrieval strategies can be categorized as:

• Extractive Summarization: Pulling key phrases or sentences directly from documents (e.g., BM25, Contriever).
• Abstractive Summarization: Generating new sentences to summarize content, using methods like LongLLMlingua and SelectiveContext.

4. Reranking and Repacking

After initial retrieval, the next step is to ensure the results are the most relevant. Reranking techniques include:

• DLM-based: Approaches leveraging various models like monoT5, monoBERT, and RankLaMA.
• TILDE: Techniques focused on improving language understanding.

Repacking is another strategy that optimizes the retrieval process. It may involve:

• Sides: Considering multiple angles of the query.
• Forward: Using previous context to inform the current response.
• Reverse: Analyzing outputs to refine future queries.

5. Chunking and Embedding

For large datasets, breaking down information into manageable pieces, known as chunking, is essential. This includes:

• Chunking Size: Determining the size of data pieces for processing.
• Sliding Windows: Moving through data sequentially to capture context.

Embedding

The embedding process converts text into numerical representations that the model can understand. Popular methods include:

• LLM-Embedder: Embedding model specifically designed for LLMs.
• various embedding models: Such as inf-to/e5 and BAAI/bg, used for different tasks.

6. Vector Database

To store and retrieve embeddings efficiently, vector databases are utilized. Some popular options include:

• Milvus: An open-source vector database designed for high-performance retrieval.
• Faiss: Developed by Facebook, it focuses on efficient similarity search.
• Weaviate, Qdrant, Chroma: Other emerging vector databases tailored for different applications.

Conclusion

Large Language Models are reshaping the landscape of information retrieval and natural language processing. Understanding the components of LLMs, from query classification to embedding and storage solutions, is crucial for leveraging their full potential in various applications. As technology continues to evolve, staying informed about these advancements will empower you to utilize LLMs effectively in your projects.