Unlocking the Power of LLMs: Exploring Different Frameworks for Building Q&A Apps

In the world of AI, building efficient and scalable Q&A (Question Answering) systems is crucial for various applications, from customer service to research. As businesses look to implement AI-powered tools, selecting the proper framework for creating Q&A apps becomes a pivotal decision. This article explores several frameworks for building Q&A systems, including the Q&A Model, Retrieval-based Models, Summarization Models, and Larger Models with Longer Token Limits. I also examine their suitability based on datasets, use cases, and user interactions.

1. Q&A Model

Overview:

The Q&A model is one of the most straightforward approaches to creating a Q&A system. This model takes a predefined context (a document, passage, or paragraph) and a user’s question. It then extracts or generates the answer directly from the context without needing external information.

Key Features:

Input: A context and a question.

Task: The model extracts or generates an answer from the given context.

Use Case: This approach is best for scenarios where a document or text already contains the information required to answer specific questions.

Example Models:

BERT (Bidirectional Encoder Representations from Transformers): A transformer-based model trained to understand the context of a sentence bidirectionally. Finetuned versions of BERT are widely used for Q&A tasks.

T5 (TexttoText Transfer Transformer): T5 converts every NLP task into a text-to-text format, including Q&A tasks, making it highly flexible and capable of generating answers directly from context.

Example:

Question: "What is Alzheimer's disease?"

Context: "Alzheimer's disease is a progressive neurodegenerative disorder that causes memory loss, confusion, and changes in behaviour..."

Answer: "Alzheimer's disease is a progressive neurodegenerative disorder..."

Use Case & Dataset Suitability:

This model works well for systems where the dataset consists of clearly defined documents, such as FAQs, product manuals, or knowledge bases. It is particularly suited for applications where the user can either input context and question or select relevant content from a list before asking their question. For example, a legal document analysis tool could allow users to input a question and upload a document, with the Q&A model extracting the answer from the provided document.

However, we face a challenge if the user only needs to ask a question and receive an answer generated from the finetuned model based solely on the training data. In this case, the context in the training data must be eliminated, leaving only a set of questions and their corresponding answers. This approach limits the model’s ability to find the most appropriate answer, as it can only select from the available answers in the dataset. This limitation leads to exploring alternative frameworks, which I will discuss next.

2. Retrieval-based Model

Overview:

A retrieval-based Q&A model involves a two-step process. First, it retrieves relevant context from a large collection of documents. Afterwards, a Q&A model processes this context to extract the final answer. This approach benefits large datasets or when the context is distributed across multiple sources.

Key Features:

Input: A question, followed by a retrieval process that fetches the relevant context.

Task: Retrieve relevant documents or passages and then extract answers from them.

Use Case: Best for applications where the dataset is large, such as scientific papers, research databases, or a collection of customer service interactions.

Example Models:

ElasticSearch: A scalable search engine that indexes documents and enables fast retrieval of relevant content. Often integrated with Q&A models like BERT to extract answers from the retrieved context.

FAISS (Facebook AI Similarity Search): A library for efficient similarity search indexing large datasets and retrieving the most relevant context based on semantic similarity.

SBERT (Sentence-BERT): An enhanced version of BERT, fine-tuned for generating sentence-level embeddings. SBERT is designed explicitly for semantic search tasks, where the goal is to retrieve documents or paragraphs based on their semantic similarity to the input question.

Example:

Question: "What is Alzheimer's disease?"

Retrieval: The system retrieves relevant documents from a collection of research papers on Alzheimer's disease using semantic search techniques.

Answer: The Q&A model processes the retrieved document and extracts the answer.

Use Case & Dataset Suitability:

The retrieval-based model is ideal for large-scale applications where the user’s question could relate to information spread across several documents. For example, a research assistant tool could allow users to ask questions about specific topics, and the system would first search a database of papers before extracting an answer. This setup is often used in sectors like healthcare, legal tech, and academic research, where users may only need to input their questions.

SBERT, in particular, is useful when the focus is on finding semantically similar passages, even in large datasets. By converting both the question and the documents into vector embeddings, SBERT enables efficient similarity comparison, making it an excellent tool for systems that require quick retrieval of the most relevant content from a vast corpus of text.

 3. Summarization Models

 Overview:

Summarization models condense large documents or sections of text into smaller, more manageable summaries. These models are particularly helpful when dealing with long articles, research papers, or books, allowing the user to understand the context before asking a question.

Key Features:

Input: A long document or multiple sections of text.

Task: Generate a concise summary of the document, which can then be used to extract relevant answers.

Use Case: This is best for scenarios where documents are too long to process in their entirety and need to be summarized for further questioning.

Example:

Input: A full research paper on Alzheimer's disease.

Output: A summary of the paper that highlights key points about Alzheimer's disease.

Use Case: Users can ask specific questions based on the summary to quickly retrieve answers.

Use Case & Dataset Suitability:

Summarization models work well with datasets consisting of long-form content, such as research articles, news articles, or books. For example, in academic research, summarization models can help users first review a summary of the paper and then ask follow-up questions. This reduces computational overhead by narrowing down the content that needs to be processed to answer questions.

4. Larger Models with Longer Token Limits

Overview:

Some newer models, such as Longformer, BigBird, and Reformer, are specifically designed to handle long documents. These models overcome the token size limitations of traditional transformer models, allowing them to process entire documents or even sections of a dataset without truncating the input.

Key Features:

Input: Large documents or datasets with longer token limits.

Task: Process long sections of text to answer questions directly from the full document.

Use Case: This is ideal for applications where the entire content needs to be processed and where long documents are the norm.

Example Models:

Longformer: A transform-based model that uses a sliding window attention mechanism to handle long documents with token lengths exceeding the typical 512 or 1024 token limits.

BigBird: A model that extends transformer architectures with sparse attention to efficiently handle long sequences.

Example:

Input: A 5,000-word scientific paper on Alzheimer's disease.

Output: The model processes the entire document and answers questions based on the full context, without truncating or summarizing the content.

 Use Case & Dataset Suitability:

This model is perfect for scientific papers, books, or any other content that exceeds the typical token limit of traditional models. A research assistant app could use this approach to allow users to upload large datasets (e.g., full research papers or lengthy textbooks) and ask detailed questions about them without having to chunk or summarize the content.

Conclusion

Each framework for creating a Q&A app serves a specific purpose based on the type of data being used and the user's needs.

Q&A Models are best when the context is already known and the task is to extract answers from the given text.

Retrieval-based models excel when working with large datasets or documents and can dynamically retrieve relevant context before answering.

 Summarization Models help when dealing with long content, condensing it to a manageable size before answering questions.

 Larger Models are suited for extremely large datasets, offering the ability to process longer documents without truncation.

The choice of model depends on the dataset type, the application scale, and how the user interacts with the system. By understanding these models, businesses can select the framework that best aligns with their goals and data constraints, improving the overall efficiency and accuracy of their Q&A applications.