The Breakthrough in Accessing Knowledge

Text is a vast repository of information in today’s digital world. Extracting knowledge from unstructured texts can be time-consuming and complex.

It starkly contrasts the structured knowledge bases such as Freebase or Wikipedia Data. They provide easier access to information, but they require meticulous organization.

It has been a longstanding quest to find a way for people to receive accurate and seamless answers when they ask questions.

Enter generative artificial intelligence! It has led to the development of sophisticated Question Answering Systems (QA), revolutionizing how we interact with information.

The Challenges of Extracting Answers From Unstructured Knowledge

Information retrieval (IR) systems of the past focused on selecting documents relevant from storage systems. The search queries were used.

However, the QA system takes it further by extracting answers specific to the documents.

The QA system has evolved to include:

Rule-based QA systems: Rules-based systems are based on patterns and rules that human experts have created. It is used to extract text answers. These systems are effective when answering simple questions. They need help to answer complex questions or provide ambiguous data.

Retrieval-Based QA Systems These systems are retrieval-based and retrieve answers using a set of predefined documents or sources. These systems use information retrieval to find relevant passages and documents that contain the answer. These systems are capable of handling a variety of questions. They are restricted to the knowledge available in the knowledge base.

Generative QA Systems Generative QA systems synthesize information from multiple sources to generate answers. They can produce humanlike responses, even if they don’t use explicit details from documents. Generative systems use natural language generation, language models, and other techniques. The goal is to give answers that are contextually relevant and coherent. They can handle open-ended and complex questions.

Open Domain Question Answering Systems (OPQA): OPQA is designed to answer multiple questions in different domains. They can:

Answers to questions asked during training

Answer novel questions using training data

Answer questions that go beyond your training scope

OPQA systems use pre-trained models as their knowledge base. They can then provide contextualized answers to many questions.

Closed Domain Question Answering Systems (CDQA): CDQA systems are designed to answer questions in documents within a specific domain. Tech support, engineering, and healthcare are examples. They offer a targeted approach to answering questions. This is done by using specialized knowledge in a particular domain. CDQA systems learn about the nuances and intricacies of a part by using the datasets. They can then provide accurate answers within the field.

Answer Extraction

A QA system retrieves relevant documents. The next step is to extract answers from these documents. For answer extraction, several approaches have been developed. Two standard procedures include:

Rule-based methods: These rely on predefined rules and patterns. The retrieval of documents is used to extract the answers. They usually involve matching keywords or phrases.

Named Entity Recognition Techniques (NER ):) Identify and classify named entities in the text. Names of people, places, and organizations are examples. You can get answers by recognizing the entities and determining their relevance.

Cosine Similarity: Cosine similarity measures the angle between vectors to determine similarity. A higher cosine similarity indicates a greater relevance for extracting and ranking answers.

Dot-Product calculates the elemental product of vectors. Dot-product values that are higher indicate a more remarkable similarity of answers. This helps in selecting and ranking relevant solutions within Q&A systems.

Two main types of QA systems are available: abstractive and extractive.

Extraction QA: Extraction QA systems extract answers from documents. This is done without altering the original text. These systems remove and identify the sentences or phrases most relevant to the question from records.

Abstractive Quality Assurance: Abstractive Quality Assurance systems synthesize information from multiple sources to generate answers. They create new sentences to convey the information required. These systems are capable of generating human-like responses.

OpenAI-powered QA Systems vs. Traditional QA Systems

The traditional QA systems rely on rules and patterns that are predefined. The ability to answer complex questions is limited.

OpenAI QA systems powered by deep learning, like those based on GPT-3.5, use the opposite. This allows them to give more accurate, contextually richer answers. All that matters is how AI answers your questions!

LLM-Based Quality Assurance (Large Language Model Based Quality Assurance)

LLM-based QA is a term used to describe Question Answering Systems that use large language models such as OpenAI’s GPT-3.5 or text-DaVinci to generate answers. These models have been trained using massive amounts of text and have a profound understanding of context and language. LLM-based QA systems are capable of handling a variety of questions and providing coherent and context-appropriate answers.

Diagram of LLM Based QA

This is a simplified flow chart of how LLM-based QA systems function:

Input question: The user provides a query or question to the system.

Context Retriever: The system searches for relevant documents and passages in its knowledge base to find the answers.

Encoding of Passages: Retrieved passages are encoded numerically, capturing the meaning and context. This encoding can be done by using techniques such as word embeddings and transformer models.

Question Coding: A numerical representation encodes the question, capturing its context and meaning.

Similarity Calculation The system calculates the similarity between each encoded passage and the encoded questions to determine the question’s relevance.

Extraction of Answer: Select the passage with the highest similarity score, as the source is most likely to be the answer. The system then extracts a response from the route identified.

Answer Generating: In some cases, the answer may need to be further processed or transformed. LLM-based QA systems can generate a humanlike solution by synthesizing the information from the retrieved text or developing a new text based on the context.