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Learning Center/Use Cases of Large Language Models (LLMs)/Lesson 0

Question Answering (Q&A) Systems with LLMs

Overview/introduction

Key ideas

Using LLMs, Q&A systems enable information retrieval, allowing your users to obtain precise answers across multiple domains through efficient and intuitive interactions.
Developers must meticulously prepare data, select strategic models, fine-tune Q&A systems, and evaluate them to ensure their responses are relevant and accurate. Good experimental performance? Deploy the model to production and monitor over time.
Evaluating Q&A systems regularly with accuracy, user satisfaction, and response time metrics is a crucial feedback loop, continually improving and refining system performance.
RAG offers a sophisticated method to enhance Q&A systems that provides your users with answers that are accurate, richly informative, and context-aware.

Question-Answering (Q&A) systems, powered by Large Language Models (LLMs), are improving how we interact with information. They enable us to quickly and accurately retrieve information from vast amounts of data, simplifying learning and decision-making.

This lesson will provide an overview of Q&A systems and their role in information retrieval and assistance.

What are question-answer (Q&A) systems?

Q&A systems are computer programs that use natural language processing (NLP) techniques to understand and answer questions posed by users. They retrieve information from large volumes of structured and unstructured data. Q&A systems are great for customer support, knowledge management, education, and other applications.

Core components of Q&A systems

Large Language Models (LLMs): LLMs such as GPT-3 are instrumental in elevating conversation flow. They enable the QA systems to understand context, remember past interactions, and generate contextually relevant responses. This component provides the human-like dialogue that differentiates modern QA systems from their predecessors.
Natural Language Processing (NLP): QA systems use NLP techniques to decipher user inputs, but integrating LLMs improves their ability to parse and generate responses with unprecedented fluency.
Knowledge base: A diverse repository, from FAQs to complex databases, provides the information backbone for the system to retrieve accurate answers. LLMs extract and synthesize information to answer user queries accurately.
User Interface (UI): The medium through which users engage with the system. It varies from text-based interfaces to voice-activated systems, impacting the user experience and system accessibility.

The role of QA systems in information retrieval and assistance

Q&A systems play a critical role in information retrieval and assistance. They allow users to quickly and easily access information that would otherwise be difficult to find. Here are the key roles of these systems:

Efficient information access: They streamline the search process using LLMs and sophisticated algorithms to retrieve precise information from sizable data repositories quickly.
Enhanced user experience: They offer intuitive interaction interfaces that improve user engagement by delivering direct and relevant responses to queries.
Support in decision-making: They empower users with the information necessary for informed decision-making by providing actionable insights in critical sectors like healthcare and business.
Learning and education: As dynamic educational tools, Q&A systems facilitate learning by offering personalized support and instant access to knowledge, catering to diverse learning preferences and needs.

Building QA systems with large language models (LLMs)

Developing Q&A systems with LLMs is a nuanced process that extends beyond mere technical implementation to include considerations for data integrity, model applicability, and ethical use.

In Course 1, “Introduction to Large Language Models (LLMs)," you learned that LLMs are models trained on many human-generated examples or datasets. With this ability, it can understand and generate human-like text, making it ideal for answering queries.

Corpus preparation and preprocessing

An exemplary Q&A system begins with rigorous corpus preparation, where diversity and quality take precedence. This step involves:

Collection: Assembling a rich dataset from varied sources ensures a comprehensive understanding of user inquiries.
Cleaning and structuring: High-quality, well-structured data forms the bedrock of effective training. It emphasizes the removal of duplicates and irrelevant information.
Bias mitigation: Identifying and correcting biases is vital for fair and unbiased model responses. Techniques such as balanced dataset creation and algorithmic fairness checks are essential.
Augmentation: Techniques like paraphrasing and back-translation enrich the dataset, improving the model's generalization ability.

LLM selection

Choosing the right LLM is pivotal. Factors include:

Application requirements: Matching the model to the system's needs, whether generating informative answers or understanding intricate user questions.
Task variant: Once you understand the requirements, it’ll inform your decision on choosing a model based on their task variant:
- Extractive QA: Models that extract the answer from a given context, such as text, tables, or HTML content. It is ideal for applications requiring precise information retrieval, such as document search and legal analysis.
- Open generative QA: Models that generate an answer in free text form using the provided context as a basis. Ideal as creative writing aids, educational tools, and anywhere nuanced, contextually driven responses are needed.
- Closed generative QA: Models that generate an answer based solely on their training and internal knowledge base; no context provided. Great for trivia and general knowledge applications, relying on the model's breadth of training data for answers.

Task Variant	Description	Example	Models	Applications
Extractive QA	Models that extract the answer from a given context, such as text, tables, or HTML content.	When the user asks, "Who wrote Hamlet?" within a context mentioning Shakespeare's works, the model would extract "Shakespeare" as the answer.	BERT, RoBERTA, DistilBERT.	Information retrieval
Open Generative QA	Here, the model generates an answer in free text form using the provided context.	Generating a summary answer from a detailed historical article.	GPT models, LLAMA, and Mistral.	Creative aids
Closed Generative QA	Here, no context is provided, and the model generates an answer based solely on its training and internal knowledge base.	User asks, "Who is the author of 1984?” Having been trained on a wide array of literary sources, the model recognizes "1984" as a significant work and generates "George Orwell" as the answer, despite no specific context being provided.	Jurassic-1, GPT models, T5, BART, Falcon, LLAMA, and Mistral.	Trivia and General knowledge

Task Variant

Description

Example

Models

Applications

Extractive QA

Models that extract the

answer from a given

context, such as text,

tables, or HTML content.

When the user asks,

"Who wrote Hamlet?"

within a context mentioning

Shakespeare's works, the

model would extract

"Shakespeare" as the answer.

BERT, RoBERTA, DistilBERT.

Information retrieval

Open Generative QA

Here, the model generates

an answer in free text

form using the provided

context.

Generating a summary

answer from a detailed

historical article.

GPT models, LLAMA,

and Mistral.

Creative aids

Closed Generative QA

Here, no context is

provided, and the model

generates an answer

based solely on its training

and internal knowledge

base.

User asks, "Who is the author

of 1984?” Having been trained

on a wide array of literary

sources, the model recognizes

"1984" as a significant work

and generates "George Orwell"

as the answer, despite no

specific context being provided.

Jurassic-1, GPT models, T5,

BART, Falcon, LLAMA, and

Mistral.

Trivia and

General knowledge

Model capabilities: Beyond functional performance, also assess operational metrics like size, latency, and tooling support for inference in line with your application requirements. For instance, you don’t want to use a pre-trained LLM that would cost a lot to deploy and host in production or take a long time to respond with answers.

Usually, selecting any high-performing pre-trained LLM should be good enough for your QA use case. If not, you can try to perform prompt engineering techniques. Here, you craft effective prompts that guide the model to generate desired responses, enhancing its relevance to specific use cases.

Fine-tuning

Fine-tuning a pre-trained model like those available from Hugging Face is a resource-efficient approach to achieving task-specific performance. This step involves:

Custom data training: Adapting the model to the nuances of your domain by training it on your Q&A dataset. If the Q&A system is targeted at a specific domain, further fine-tuning the LLM on domain-specific data can significantly improve its accuracy and relevance.

Evaluate the performance of the fine-tuned LLM against the base LLM to ensure significant improvement. See some evaluation practices in the next section.

Refer to Course 1: Lesson 4, “Training and Fine-Tuning Large Language Models (LLMs),” to learn more about fine-tuning LLMs.

Beyond fine-tuning and evaluation, there are also key notes on deploying and monitoring the production performance of these QA systems.

Techniques for evaluating and improving the performance of QA systems

Evaluating and enhancing the performance of Q&A systems involves a multifaceted approach, ensuring these systems are accurate and user-friendly.

Here's how to assess and refine your Q&A system:

Key metrics for Q&A evaluation:

Response relevance: Assess how well the system's answers align with the query's context and intent.
Sentiment analysis: Evaluate the emotional tone of both queries and responses, ensuring appropriateness for customer interactions.
Content compliance: Monitor for "jailbreak" instances where responses deviate from expected norms or rules, ensuring content remains on-topic and within ethical guidelines.
Toxicity detection: Implement checks for harmful or offensive language to maintain a safe interaction environment.

LangKit is an open-source text metrics toolkit for evaluating and monitoring language models. It features those metrics and some for evaluating LLM-based Q&A systems.

Monitor user experience:

Use tools like LangKit for monitoring trends and anomalies in user interactions, applying sentiment analysis to gather comprehensive feedback.
Conduct A/B testing to empirically determine the impact of prompt modifications, using statistical analysis to validate the findings.

Security and explainability:

Ensure system decisions are transparent and understandable, addressing the security of user data, adversarial attacks, and the rationale behind responses.

LangKit reduces the risk of running LLMs in production. If the Q&A system is regulated, it helps define regulatory guarantees as code and replicates them across any large and small LLM.

Testing and optimization:

Use automated testing tools to simulate diverse queries, identifying areas for improvement in accuracy and response time.
Vigilantly assess and address biases for fairness and inclusivity in responses.
Focus on optimizing response times through model efficiency techniques (model pruning, quantization) and streamlined data retrieval processes.

Using Retrieval Augmented Generation (RAG):

In Course 1: Lesson 5, we looked at Retrieval Augmented Generation (RAG). This technique combines the strengths of retrieval-based and generative approaches in LLM to enhance the performance of Q&A systems. How does it help?

Retrieval phase: The system searches the knowledge database or document set to find content relevant to the user's question.
Generation phase: After retrieving relevant documents, a generative model like GPT synthesizes the information from these documents to generate a coherent and contextually appropriate answer.

Emerging trends in LLM-powered Q&A systems

Multimodal Q&A: These advanced systems use multimodal LLMs to process and interpret multiple modalities (text, speech, and visual inputs). For instance, a user can ask a cooking-related question while showing an image of available ingredients, and the system can provide a spoken recipe suggestion.
Domain-specific Q&A: Tailored to specific sectors like healthcare and finance, these systems deliver highly accurate and relevant answers by training on specialized datasets. An example includes a financial advisory chatbot that can offer personalized investment advice based on current market trends.

In the next lesson, you will learn how LLMs have improved sentiment analysis applications.