Document Retrieval and Question Answering with LLMs: The Future of Information Retrieval

Document retrieval and question answering with Large Language Models (LLMs) simply involve using advanced neural networks to understand and respond to user queries by retrieving relevant documents from a vast corpus and extracting precise information.

But Wait, What Are LLMs Anyway?

You might be wondering, “What makes these Large Language Models so special?” Well, they’re not your average algorithms. LLMs are like the rockstars of the AI world, trained on an insane amount of text data (think entire libraries or the whole internet!).

This training is what gives them their superpower: understanding and generating human-like text.

You see, LLMs are built on deep neural networks, and those networks are massive. We’re talking billions (yes, with a ‘B’) of parameters. The parameters are like knobs and dials(weights and biases). The LLM adjusts them in training to learn the details of language.

Remember those embeddings we talked about earlier? Those numerical fingerprints of words and documents? Well, LLMs are the masterminds behind those.

They learn to map words and phrases into a high-dimensional space. Similar concepts are clustered together.

This allows them to grasp the nuances of meaning and context. It is key for tasks like finding documents and answering questions.

Transformer Architecture: The Secret Sauce

One key innovation is behind the recent surge in LLM capabilities. It is the Transformer architecture. Ever heard of GPT-3 or BERT? Those are prime examples of transformer-based LLMs.

The Transformer architecture is like a supercharged engine for LLMs. It uses self-attention. This lets the model weigh the importance of words in a sentence. It helps when understanding its meaning. This makes LLMs incredibly good at tasks like:

• NLU
• Text generation
• Translation
• Summarization

What is Retrieval Augmented Generation (RAG)?

We discussed this in our previous article. Retrieval augmented generation (RAG) combines the strengths of information retrieval and generative AI. It retrieves relevant documents from a large collection.

Then, an LLM generates a precise answer based on this information.

Under the Hood: How The LLM Does Its Thing 🔧

• Document Embedding: Documents are transformed into numerical representations (vectors) that capture their semantic meaning. This is often done using tools like the Universal Sentence Encoder or specialized models like Instructor.
• Vector Database: These vectors are stored in a vector database, such as Chroma or Pinecone, designed for fast similarity searches.
• Query Embedding: When a user asks a question, it is also converted into a vector using the same embedding model.
• Similarity Search: The vector database finds the most similar document vectors to the query vector, indicating which documents are most relevant to the question.

The LLM uses the retrieved documents and the query to generate a full answer. You can do this step using a simple prompt. Or, you can use a more complex RAG pipeline. The Google Cloud Generative AI article describes the RAG pipeline well.

Get Your Hands Dirty: Let’s run a simple document retrieval Code with LangChain

 from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
 
# Load your documents (use the right loader for your file type)
loader = TextLoader('my_codebase.txt')
documents = loader.load()
 
# Split into manageable chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
 
# Create embeddings and build your vector database
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(docs, embeddings)
 
# Set up your RAG QA system
qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=db)
 
# Ask away!
query = "How do I implement a custom loss function in PyTorch?"
answer = qa.run(query)
print(answer)

Okay, now that you know the basics of LLM, RAG, and how to code a simple document retrieval system, let’s dive deeper into the retrieval side of things!

We’ve got those powerhouse LLMs ready to generate answers, but first, they need the right information to work with. That’s where document retrieval comes into play.

What is Document Retrieval: Finding the Right Info in the Haystack

Think of document retrieval as the LLM’s trusted sidekick or assistant, helping it find the right information.

It’s the process of sifting through a vast collection of documents (which could be your codebase, a library of research papers, or even the entire Internet!) to find the ones that are most relevant to a user’s query.

Remember how we talked about embeddings? Those numerical representations of words and documents?

Well, they’re the key to efficient document retrieval. We convert documents and queries into vectors.

Then, we use cosine similarity to measure their closeness in meaning. This lets us rank documents by relevance. We can then quickly find the best candidates to answer the user’s question.

The Retrieval Toolbox: Different Methods of Document Retrieval System

Several methods can be used to retrieve documents. Each method has its own strengths and weaknesses.

The retrieval method depends on many factors. These factors include:

• The size and complexity of the document collection.
• The types of queries you expect, and
• The available computational resources.

Putting it All Together: Retrieval-Augmented Generation (RAG) in Action

Document retrieval is the crucial first step in a RAG system. Once the relevant documents are found, the LLM can analyze them. It will then generate a response based on the found information.

Remember our LangChain example above? We used the VectorDBQA.similarity_search method under the hood. It does exactly this type of retrieval. It finds the document chunks that are most similar to the query. It then passes them to the LLM to generate answers.

Future of Document Retrieval and Question Answering

LLMs are constantly changing. New research is pushing the limits. It is finding documents and answering questions.

ReSP: Retrieve, Summarize, Plan

Researchers at Ant Group have introduced ReSP. It is an innovative approach that tackles the challenges of multi-hop question answering.

Here, a question needs information from many documents to be answered correctly. ReSP uses a clever “dual-function summarizer” to condense information from retrieved documents. It focuses on the main and sub-questions.

This helps the LLM avoid getting swamped by too much context. It stops it from going down repetitive or unnecessary paths.

CiteME and CiteAgent: Making LLMs accountable.

More exciting news! It comes from researchers at the University of Tübingen and Princeton University. They’ve created CiteME, a key dataset that tests how well LLMs can attribute claims to their sources.

This is a crucial step toward making LLM-generated information more reliable and trustworthy.

To tackle this challenge, they also made CiteAgent. It is an LLM-powered agent. It can search and read papers to find the right citations.

CiteAgent is still a work in progress. It shows promise and opens the door to a future. In this future, LLMs can automatically verify their claims and give accurate references.

Evaluating RAG Metrics in the Telecom Domain

Researchers at Ericsson Research are also making strides in evaluating RAG systems. They are focusing on the telecom domain.

They’ve modified the RAG Assessment (RAGAS) library. It now gives more detailed insights into how LLMs evaluate RAG outputs.

Their work shows why we need to fine-tune for specific topics. It also shows the challenges of evaluating complex technical information.

Establishing Knowledge Preference in Language Models

The researchers are from the University of Illinois and the University of Virginia. They study how LLMs rank preferences. They study how to rank what LLMs prefer. They’ve made a framework to help LLMs prioritize sources of knowledge.

These include user instructions, retrieved context, and the model’s own knowledge. This is crucial. It ensures that LLMs respect user preferences and give accurate answers. This happens even when they have conflicting information.

Final Thoughts

These are a few examples of the groundbreaking research happening in the LLM space. As AI developers and enthusiasts, we’re lucky to see this amazing progress firsthand. By staying informed about these advances, we can keep pushing the limits of what’s possible with LLMs and build even more powerful and reliable AI systems.

Let me know in the comment section if you have experimented with different document retrieval methods. What challenges have you faced, and how did you overcome them? Let’s share our experiences and learn from each other!