50+ Generative AI Interview Questions

Generative AI is a newly developed field booming exponentially with job opportunities. Companies are looking for candidates with the necessary technical abilities and real-world experience building AI models. This list of interview questions includes descriptive answer questions, short answer questions, and MCQs that will prepare you well for any generative AI interview. These questions cover everything from the basics of AI to putting complicated algorithms into practice. So let’s get started with Generative AI Interview Questions!

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GenAI Interview Questions

Here’s our comprehensive list of questions and answers on Generative AI that you must know before your next interview.

Generative AI Interview Questions Related to Neural Networks

Q1. What are Transformers?

Answer: A Transformer is a type of neural network architecture introduced in the 2017 paper “Attention Is All You Need” by Vaswani et al. It has become the backbone for many state-of-the-art natural language processing models. 

Here are the key points about Transformers:

• Architecture: Unlike recurrent neural networks (RNNs), which process input sequences sequentially, transformers handle input sequences in parallel via a self-attention mechanism.
• Key components:
  • Encoder-Decoder structure
  • Multi-head attention layers
  • Feed-forward neural networks
  • Positional encodings
• Self-attention: This feature enables the model to efficiently capture long-range relationships by assessing the relative relevance of various input components as it processes each element.
• Parallelisation: Transformers can handle all input tokens concurrently, which speeds up training and inference times compared to RNNs.
• Scalability: Transformers can handle longer sequences and larger datasets more effectively than previous architectures.
• Versatility: Transformers were first created for machine translation, but they have now been modified for various NLP tasks, including computer vision applications.
• Impact: Transformer-based models, including BERT, GPT, and T5, are the basis for many generative AI applications and have broken records in various language tasks.

Transformers have revolutionized NLP and continue to be crucial components in the development of advanced AI models.

Q2. What is Attention? What are some attention mechanism types?

Answer: Attention is a technique used in generative AI and neural networks that allows models to focus on specific input areas when generating output. It enables the model to dynamically ascertain the relative importance of each input component in the sequence instead of considering all the input components similarly.

1. Self-Attention:

Also referred to as intra-attention, self-attention enables a model to focus on various points within an input sequence. It plays a crucial role in transformer architectures.

How does it work?

• Three vectors are created for each element in a sequence: query (Q), Key (K), and Value (V).
• Attention scores are computed by taking the dot product of the Query with all Key vectors.
• These scores are normalized using softmax to get attention weights.
• The final output is a weighted sum of the Value vectors, using the attention weights.

Benefits:

• Captures long-range dependencies in sequences.
• Allows parallel computation, making it faster than recurrent methods.
• Provides interpretability through attention weights.

2. Multi-Head Attention:

This technique enables the model to attend to data from many representation subspaces by executing numerous attention processes simultaneously.

How does it work?

• The input is linearly projected into multiple Query, Key, and Value vector sets.
• Self-attention is performed on each set independently.
• The results are concatenated and linearly transformed to produce the final output.

Benefits:

• Allows the model to jointly attend to information from different perspectives.
• Improves the representation power of the model.
• Stabilizes the learning process of attention mechanisms.

3. Cross-Attention:

This technique enables the model to process one sequence while attending to information from another and is frequently utilised in encoder-decoder systems.

How does it work?

• Queries come from one sequence (e.g., the decoder), while Keys and Values come from another (e.g., the encoder).
• The attention mechanism then proceeds similarly to self-attention.

Benefits:

• Enables the model to focus on relevant input parts when generating each part of the output.
• Crucial for tasks like machine translation and text summarization.

4. Causal Attention:

Also referred to as veiled attention, causal attention is a technique used in autoregressive models to stop the model from focussing on tokens that are presented in the future.

How does it work?

• Similar to self-attention, but with a mask applied to the attention scores.
• The mask sets attention weights for future tokens to negative infinity (or a very large negative number).
• This ensures that when generating a token, the model only considers previous tokens.

Benefits:

• Enables autoregressive generation.
• Maintains the temporal order of sequences.
• Used in language models like GPT.

5. Global Attention:

• Attends to all positions in the input sequence.
• Provides a comprehensive view of the entire input.
• Can be computationally expensive for very long sequences.

6. Local Attention:

• Attends only to a fixed-size window around the current position.
• More efficient for long sequences.
• Can be combined with global attention for a balance of efficiency and comprehensive context.

How Does Local Attention Work?

• Defines a fixed window size (e.g., k tokens before and after the current token).
• Computes attention only within this window.
• Can use various strategies to define the local context (fixed-size windows, Gaussian distributions, etc.).

Benefits of Local Attention:

• Reduces computational complexity for long sequences.
• Can capture local patterns effectively.
• Useful in scenarios where nearby context is most relevant.

These attention processes have advantages and work best with particular tasks or model architectures. The task’s particular needs, the available processing power, and the intended trade-off between model performance and efficiency are typically factors that influence the choice of attention mechanism.

Q3. How and why are transformers better than RNN architectures?

Answer: Transformers have largely superseded Recurrent Neural Network (RNN) architectures in many natural language processing tasks. Here’s an explanation of how and why transformers are generally considered better than RNNs:

Parallelization:

How: Transformers process entire sequences in parallel.

Why better:

• RNNs process sequences sequentially, which is slower.
• Transformers can leverage modern GPU architectures more effectively, resulting in significantly faster training and inference times.

Long-range dependencies:

How: Transformers use self-attention to directly model relationships between all pairs of tokens in a sequence.

Why better:

• Because of the vanishing gradient issue, RNNs have difficulty handling long-range dependencies.
• Transformers perform better on tasks that require a grasp of greater context because they can easily capture both short—and long-range dependencies.

Attention mechanisms:

How: Transformers use multi-head attention, allowing them to focus on different parts of the input for different purposes simultaneously.

Why better:

• Provides a more flexible and powerful way to model complex relationships in the data.
• Offers better interpretability as attention weights can be visualized.

Positional encodings:

How: Transformers use positional encodings to inject sequence order information.

Why better:

• Allows the model to understand sequence order without recurrence.
• Provides flexibility in handling variable-length sequences.

Scalability:

How: Transformer architectures can be easily scaled up by increasing the number of layers, attention heads, or model dimensions.

Why better:

• This scalability has led to state-of-the-art performance in many NLP tasks.
• Has enabled the development of increasingly large and powerful language models.

Transfer learning:

How: Pre-trained transformer models can be fine-tuned for various downstream tasks.

Why better:

• This transfer learning capability has revolutionized NLP, allowing for high performance even with limited task-specific data.
• RNNs don’t transfer as effectively to different tasks.

Consistent performance across sequence lengths:

How: Transformers maintain performance for both short and long sequences.

Why better:

• RNNs often struggle with very long sequences due to gradient issues.
• Transformers can handle variable-length inputs more gracefully.

RNNs still have a role, even if transformers have supplanted them in many applications. This is especially true when computational resources are scarce or the sequential character of the data is essential. However, transformers are now the recommended design for most large-scale NLP workloads because of their better performance and efficiency.

Q4. Where are Transformers used?

Answer: These models are significant advancements in natural language processing, all built on the transformer architecture.

BERT (Bidirectional Encoder Representations from Transformers):

• Architecture: Uses only the encoder part of the transformer.
• Key feature: Bidirectional context understanding.
• Pre-training tasks: Masked Language Modeling and Next Sentence Prediction.
• Applications:
  • Question answering
  • Sentiment analysis
  • Named Entity Recognition
  • Text classification

GPT (Generative Pre-trained Transformer):

• Architecture: Uses only the decoder part of the transformer.
• Key feature: Autoregressive language modeling.
• Pre-training task: Next token prediction.
• Applications:
  • Text generation
  • Dialogue systems
  • Summarization
  • Translation

T5 (Text-to-Text Transfer Transformer):

• Architecture: Encoder-decoder transformer.
• Key feature: Frames all NLP tasks as text-to-text problems.
• Pre-training task: Span corruption (similar to BERT’s masked language modeling).
• Applications:
  • Multi-task learning
  • Transfer learning across various NLP tasks

RoBERTa (Robustly Optimized BERT Approach):

• Architecture: Similar to BERT, but with optimized training process.
• Key improvements: Longer training, larger batches, more data.
• Applications: Similar to BERT, but with improved performance.

XLNet:

• Architecture: Based on transformer-XL.
• Key feature: Permutation language modeling for bidirectional context without masks.
• Applications: Similar to BERT, with potentially better handling of long-range dependencies.

Generative AI Interview Questions Related to LLMs

Q5. What is a Large Language Model (LLM)?

Answer: A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data — hence the name “large.” LLMs are built on machine learning; specifically, a type of neural network called a transformer model.

To put it more simply, an LLM is a computer program that has been fed enough instances to identify and comprehend complicated data, like human language. Thousands or millions of megabytes of text from the Internet are used to train a large number of LLMs. However, an LLM’s programmers may choose to employ a more carefully selected data set because the caliber of the samples affects how successfully the LLMs learn natural language.

A foundational LLM (Large Language Model) is a pre-trained model trained on a large and diverse corpus of text data to understand and generate human language. This pre-training allows the model to learn the structure, nuances, and patterns of language but in a general sense, without being tailored to any specific tasks or domains. Examples include GPT-3 and GPT-4.

A fine-tuned LLM is a foundational LLM that has undergone additional training on a smaller, task-specific dataset to enhance its performance for a particular application or domain. This fine-tuning process adjusts the model’s parameters to better handle specific tasks, such as sentiment analysis, machine translation, or question answering, making it more effective and accurate.

Q6. What are LLMs used for?

Answer: Numerous tasks are trainable for LLMs. Their use in generative AI, where they may generate text in response to prompts or questions, is one of its most well-known applications. For example, the publicly accessible LLM ChatGPT may produce poems, essays, and other textual formats based on input from the user.

Any large, complex data set can be used to train LLMs, including programming languages. Some LLMs can help programmers write code. They can write functions upon request — or, given some code as a starting point, they can finish writing a program. LLMs may also be used in:

• Sentiment analysis
• DNA research
• Customer service
• Chatbots
• Online search

Examples of real-world LLMs include ChatGPT (from OpenAI), Gemini (Google) , and Llama (Meta). GitHub’s Copilot is another example, but for coding instead of natural human language.

Q7. What are some advantages and limitations of LLMs?

Answer: A key characteristic of LLMs is their ability to respond to unpredictable queries. A traditional computer program receives commands in its accepted syntax or from a certain set of inputs from the user. A video game has a finite set of buttons; an application has a finite set of things a user can click or type, and a programming language is composed of precise if/then statements.

On the other hand, an LLM can utilise data analysis and natural language responses to provide a logical response to an unstructured prompt or query. An LLM might respond to a question like “What are the four greatest funk bands in history?” with a list of four such bands and a passably strong argument for why they are the best, but a standard computer program would not be able to identify such a prompt.

However, the accuracy of the information provided by LLMs is only as good as the data they consume. If they are given erroneous information, they will respond to user enquiries with misleading information. LLMs can also “hallucinate” occasionally, fabricating facts when they are unable to provide a precise response. For instance, the 2022 news outlet Fast Company questioned ChatGPT about Tesla’s most recent financial quarter. Although ChatGPT responded with a comprehensible news piece, a large portion of the information was made up.

Q8. What are different LLM architectures?

Answer: The Transformer architecture is widely used for LLMs due to its parallelizability and capacity, enabling the scaling of language models to billions or even trillions of parameters.

Existing LLMs can be broadly classified into three types: encoder-decoder, causal decoder, and prefix decoder.

Encoder-Decoder Architecture

Based on the vanilla Transformer model, the encoder-decoder architecture consists of two stacks of Transformer blocks – an encoder and a decoder.

The encoder utilizes stacked multi-head self-attention layers to encode the input sequence and generate latent representations. The decoder performs cross-attention on these representations and generates the target sequence.

Encoder-decoder PLMs like T5 and BART have demonstrated effectiveness in various NLP tasks. However, only a few LLMs, such as Flan-T5, are built using this architecture.

Causal Decoder Architecture

The causal decoder architecture incorporates a unidirectional attention mask, allowing each input token to attend only to past tokens and itself. The decoder processes both input and output tokens in the same manner.

The GPT-series models, including GPT-1, GPT-2, and GPT-3, are representative language models built on this architecture. GPT-3 has shown remarkable in-context learning capabilities.

Various LLMs, including OPT, BLOOM, and Gopher have widely adopted causal decoders.

Prefix Decoder Architecture

The prefix decoder architecture, also known as the non-causal decoder, modifies the masking mechanism of causal decoders to enable bidirectional attention over prefix tokens and unidirectional attention on generated tokens.

Like the encoder-decoder architecture, prefix decoders can encode the prefix sequence bidirectionally and predict output tokens autoregressively using shared parameters.

Instead of training from scratch, a practical approach is to train causal decoders and convert them into prefix decoders for faster convergence. LLMs based on prefix decoders include GLM130B and U-PaLM.

All three architecture types can be extended using the mixture-of-experts (MoE) scaling technique, which sparsely activates a subset of neural network weights for each input.

This approach has been used in models like Switch Transformer and GLaM, and increasing the number of experts or the total parameter size has shown significant performance improvements.

Encoder only Architecture

The encoder-only architecture uses only the encoder stack of Transformer blocks, focusing on understanding and representing input data through self-attention mechanisms. This architecture is ideal for tasks that require analyzing and interpreting text rather than generating it.

Key Characteristics:

• Utilizes self-attention layers to encode the input sequence.
• Generates rich, contextual embeddings for each token.
• Optimized for tasks like text classification and named entity recognition (NER).

Examples of Encoder-Only Models:

• BERT (Bidirectional Encoder Representations from Transformers): Excels in understanding the context by jointly conditioning on left and right context.
• RoBERTa (Robustly Optimized BERT Pretraining Approach): Enhances BERT by optimizing the training procedure for better performance.
• DistilBERT: A smaller, faster, and more efficient version of BERT.

Q9. What are hallucinations in LLMs?

Answer: Large Language Models (LLMs) are known to have “hallucinations.” This is a behavior in that the model speaks false knowledge as if it is accurate. A large language model is a trained machine-learning model that generates text based on your prompt. The model’s training provided some knowledge derived from the training data we provided. It is difficult to tell what knowledge a model remembers or what it does not. When a model generates text, it can’t tell if the generation is accurate.

In the context of LLMs, “hallucination” refers to a phenomenon where the model generates incorrect, nonsensical, or unreal text. Since LLMs are not databases or search engines, they would not cite where their response is based. These models generate text as an extrapolation from the prompt you provided. The result of extrapolation is not necessarily supported by any training data, but is the most correlated from the prompt.

Hallucination in LLMs is not much more complex than this, even if the model is much more sophisticated. From a high level, hallucination is caused by limited contextual understanding since the model must transform the prompt and the training data into an abstraction, in which some information may be lost. Moreover, noise in the training data may also provide a skewed statistical pattern that leads the model to respond in a way you do not expect.

Q10. How can you use Hallucinations?

Answer: Hallucinations could be seen as a characteristic of huge language models. If you want the models to be creative, you want to see them have hallucinations. For instance, if you ask ChatGPT or other large language models to provide you with a fantasy story plot, you want it to create a fresh character, scene, and storyline rather than copying an already-existing one. This is only feasible if the models don’t search through the training data.

You could also want hallucinations when seeking diversity, such as when soliciting ideas. It’s similar to asking models to come up with ideas for you. Though not precisely the same, you want to offer variations on the current concepts that you would find in the training set. Hallucinations allow you to consider alternative options.

Many language models have a “temperature” parameter. You can control the temperature in ChatGPT using the API instead of the web interface. This is a random parameter. A higher temperature can introduce more hallucinations.

Q11. How to mitigate Hallucinations?

Answer: Language models are not databases or search engines. Illusions are inevitable. What irritates me is that the models produce difficult-to-find errors in the text.

If the delusion was brought on by tainted training data, you can clean up the data and retrain the model. Nevertheless, the majority of models are too big to train independently. Using commodity hardware can make it impossible to even fine-tune an established model. If something went horribly wrong, asking the model to regenerate and including humans in the outcome would be the best mitigating measures.

Controlled creation is another way to prevent hallucinations. It entails giving the model sufficient information and limitations in the prompt. As such, the model’s ability to hallucinate is restricted. Prompt engineering is used to define the role and context for the model, guiding the generation and preventing unbounded hallucinations.

Also Read: Top 7 Strategies to Mitigate Hallucinations in LLMs

Generative AI Interview Questions Related to Prompt Engineering

Q12. What is prompt engineering?

Answer: Prompt engineering is a practice in the natural language processing field of artificial intelligence in which text describes what the AI demands to do. Guided by this input, the AI generates an output. This output could take different forms, with the intent to use human-understandable text conversationally to communicate with models. Since the task description is embedded in the input, the model performs more flexibly with possibilities.

Q13. What are prompts?

Answer: Prompts are detailed descriptions of the desired output expected from the model. They are the interaction between a user and the AI model. This should give us a better understanding of what engineering is about.

Q14. How to engineer your prompts?

Answer: The quality of the prompt is critical. There are ways to improve them and get your models to improve outputs. Let’s see some tips below:

• Role Playing: The idea is to make the model act as a specified system. Thus creating a tailored interaction and targeting a specific result. This saves time and complexity yet achieves tremendous results. This could be to act as a teacher, code editor, or interviewer.
• Clearness: This means removing ambiguity. Sometimes, in trying to be detailed, we end up including unnecessary content. Being brief is an excellent way to achieve this.
• Specification: This is related to role-playing, but the idea is to be specific and channeled in a streamlined direction, which avoids a scattered output.
• Consistency: Consistency means maintaining flow in the conversation. Maintain a uniform tone to ensure legibility.

Also Read: 17 Prompting Techniques to Supercharge Your LLMs

Q15. What are different Prompting techniques?

Answer: Different techniques are used in writing prompts. They are the backbone.

1. Zero-Shot Prompting

Zero-shot provides a prompt that is not part of the training yet still performing as desired. In a nutshell, LLMs can generalize.

For Example: if the prompt is: Classify the text into neutral, negative, or positive. And the text is: I think the presentation was awesome.

Sentiment:

Output: Positive

The knowledge of the meaning of “sentiment” made the model zero-shot how to classify the question even though it has not been given a bunch of text classifications to work on. There might be a pitfall since no descriptive data is provided in the text. Then we can use few-shot prompting.

2. Few-Shot Prompting/In-Context Learning

In an elementary understanding, the few-shot uses a few examples (shots) of what it must do. This takes some insight from a demonstration to perform. Instead of relying solely on what it is trained on, it builds on the shots available.

3. Chain-of-thought (CoT)

CoT allows the model to achieve complex reasoning through middle reasoning steps. It involves creating and improving intermediate steps called “chains of reasoning” to foster better language understanding and outputs. It can be like a hybrid that combines few-shot on more complex tasks.

Generative AI Interview Questions Related to RAG

Q16. What is RAG (Retrieval-Augmented Generation)?

Answer: Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

Q17. Why is Retrieval-Augmented Generation important?

Answer: Intelligent chatbots and other applications involving natural language processing (NLP) rely on LLMs as a fundamental artificial intelligence (AI) technique. The objective is to develop bots that, through cross-referencing reliable knowledge sources, can respond to user enquiries in a variety of scenarios. Regretfully, LLM replies become unpredictable due to the nature of LLM technology. LLM training data also introduces a cut-off date on the information it possesses and is stagnant.

Known challenges of LLMs include:

• Presenting false information when it does not have the answer.
• Presenting out-of-date or generic information when the user expects a specific, current response.
• Creating a response from non-authoritative sources.
• Creating inaccurate responses due to terminology confusion, wherein different training sources use the same terminology to talk about different things.

The Large Language Model can be compared to an overzealous new hire who refuses to keep up with current affairs but will always respond to enquiries with complete assurance. Unfortunately, you don’t want your chatbots to adopt such a mindset since it might harm consumer trust!

One method for addressing some of these issues is RAG. It reroutes the LLM to obtain pertinent data from reliable, pre-selected knowledge sources. Users learn how the LLM creates the response, and organizations have more control over the resulting text output.

Q18. What are the benefits of Retrieval-Augmented Generation?

Answer: RAG Technology in Generative AI Implementation

• Cost-effective: RAG technology is a cost-effective method for introducing new data to generative AI models, making it more accessible and usable.
• Current information: RAG allows developers to provide the latest research, statistics, or news to the models, enhancing their relevance.
• Enhanced user trust: RAG allows the models to present accurate information with source attribution, increasing user trust and confidence in the generative AI solution.
• More developer control: RAG allows developers to test and improve chat applications more efficiently, control information sources, restrict sensitive information retrieval, and troubleshoot if the LLM references incorrect information sources.

Generative AI Interview Questions Related to LangChain

Q19. What is LangChain?

Answer: An open-source framework called LangChain creates applications based on large language models (LLMs). Large deep learning models known as LLMs are pre-trained on vast amounts of data and can produce answers to user requests, such as generating images from text-based prompts or providing answers to enquiries. To increase the relevance, accuracy, and degree of customisation of the data produced by the models, LangChain offers abstractions and tools. For instance, developers can create new prompt chains or alter pre-existing templates using LangChain components. Additionally, LangChain has parts that let LLMs use fresh data sets without having to retrain.

Q20. Why is LangChain important?

Answer: LangChain: Enhancing Machine Learning Applications

• LangChain streamlines the process of developing data-responsive applications, making prompt engineering more efficient.
• It allows organizations to repurpose language models for domain-specific applications, enhancing model responses without retraining or fine-tuning.
• It allows developers to build complex applications referencing proprietary information, reducing model hallucination and improving response accuracy.
• LangChain simplifies AI development by abstracting the complexity of data source integrations and prompt refining.
• It provides AI developers with tools to connect language models with external data sources, making it open-source and supported by an active community.
• LangChain is available for free and provides support from other developers proficient in the framework.

Generative AI Interview Questions Related to LlamaIndex

Q21. What is LlamaIndex?

Answer: A data framework for applications based on Large Language Models (LLMs) is called LlamaIndex. Large-scale public datasets are used to pre-train LLMs like GPT-4, which gives them amazing natural language processing skills right out of the box. Nevertheless, their usefulness is restricted in the absence of your personal information.

Using adaptable data connectors, LlamaIndex enables you to import data from databases, PDFs, APIs, and more. Indexing of this data results in intermediate representations that are LLM-optimized. Afterwards, LlamaIndex enables natural language querying and communication with your data through chat interfaces, query engines, and data agents with LLM capabilities. Your LLMs may access and analyse confidential data on a massive scale with it, all without having to retrain the model using updated data.

Q22. How LlamaIndex Works?

Answer: LlamaIndex uses Retrieval-Augmented Generation (RAG) technologies. It combines a private knowledge base with massive language models. The indexing and querying stages are typically its two phases.

Indexing stage

During the indexing stage, LlamaIndex will effectively index private data into a vector index. This stage aids in building a domain-specific searchable knowledge base. Text documents, database entries, knowledge graphs, and other kind of data can all be entered.

In essence, indexing transforms the data into numerical embeddings or vectors that represent its semantic content. It permits fast searches for similarities throughout the content.

Querying stage

Based on the user’s question, the RAG pipeline looks for the most pertinent data during querying. The LLM is then provided with this data and the query to generate a correct result.

Through this process, the LLM can obtain up-to-date and relevant material not covered in its first training. At this point, the primary problem is retrieving, organising, and reasoning across potentially many information sources.

Generative AI Interview Questions Related to Fine-Tuning

Q23. What is fine-tuning in LLMs?

Answer: While pre-trained language models are prodigious, they are not inherently experts in any specific task. They may have an incredible grasp of language. Still, they need some LLMs fine-tuning, a process where developers enhance their performance in tasks like sentiment analysis, language translation, or answering questions about specific domains. Fine-tuning large language models is the key to unlocking their full potential and tailoring their capabilities to specific applications

Fine-tuning is like providing a finishing touch to these versatile models. Imagine having a multi-talented friend who excels in various areas, but you need them to master one particular skill for a special occasion. You would give them some specific training in that area, right? That’s precisely what we do with pre-trained language models during fine-tuning.

Also Read: Fine-Tuning Large Language Models

Q24. What is the need for fine tuning LLMs?

Answer: While pre-trained language models are remarkable, they are not task-specific by default. Fine-tuning large language models is adapting these general-purpose models to perform specialized tasks more accurately and efficiently. When we encounter a specific NLP task like sentiment analysis for customer reviews or question-answering for a particular domain, we need to fine-tune the pre-trained model to understand the nuances of that specific task and domain.

The benefits of fine-tuning are manifold. Firstly, it leverages the knowledge learned during pre-training, saving substantial time and computational resources that would otherwise be required to train a model from scratch. Secondly, fine-tuning allows us to perform better on specific tasks, as the model is now attuned to the intricacies and nuances of the domain it was fine-tuned for.

Q25. What is the difference between fine tuning and training LLMs?

Answer: Fine-tuning is a technique used in model training, distinct from pre-training, which is the initializing model parameters. Pre-training begins with random initialization of model parameters and occurs iteratively in two phases: forward pass and backpropagation. Conventional supervised learning (SSL) is used for pre-training models for computer vision tasks, such as image classification, object detection, or image segmentation.

LLMs are typically pre-trained through self-supervised learning (SSL), which uses pretext tasks to derive ground truth from unlabeled data. This allows for the use of massively large datasets without the burden of annotating millions or billions of data points, saving labor but requiring large computational resources. Fine-tuning entails techniques to further train a model whose weights have been updated through prior training, tailoring it on a smaller, task-specific dataset. This approach provides the best of both worlds, leveraging the broad knowledge and stability gained from pre-training on a massive set of data and honing the model’s understanding of more detailed concepts.

Q26. What are the different types of fine-tuning?

Answer: Fine-tuning Approaches in Generative AI

Supervised Fine-tuning:

• Trains the model on a labeled dataset specific to the target task.
• Example: Sentiment analysis model trained on a dataset with text samples labeled with their corresponding sentiment.

Transfer Learning:

• Allows a model to perform a task different from the initial task.
• Leverages knowledge from a large, general dataset to a more specific task.

Domain-specific Fine-tuning:

• Adapts the model to understand and generate text specific to a particular domain or industry.
• Example: A medical app chatbot trained with medical records to adapt its language understanding capabilities to the health field.

Parameter-Efficient Fine-Tauning (PEFT)

Parameter-Efficient Fine-Tuning (PEFT) is a method designed to optimize the fine-tuning process of large-scale pre-trained language models by updating only a small subset of parameters. Traditional fine-tuning requires adjusting millions or even billions of parameters, which is computationally expensive and resource-intensive. PEFT techniques, such as low-rank adaptation (LoRA), adapter modules, or prompt tuning, allow for significant reductions in the number of trainable parameters. These methods introduce additional layers or modify specific parts of the model, enabling fine-tuning with much lower computational costs while still achieving high performance on targeted tasks. This makes fine-tuning more accessible and efficient, particularly for researchers and practitioners with limited computational resources.

Supervised Fine-Tuning (SFT)

Supervised Fine-Tuning (SFT) is a critical process in refining pre-trained language models to perform specific tasks using labelled datasets. Unlike unsupervised learning, which relies on large amounts of unlabelled data, SFT uses datasets where the correct outputs are known, allowing the model to learn the precise mappings from inputs to outputs. This process involves starting with a pre-trained model, which has learned general language features from a vast corpus of text, and then fine-tuning it with task-specific labelled data. This approach leverages the broad knowledge of the pre-trained model while adapting it to excel at particular tasks, such as sentiment analysis, question answering, or named entity recognition. SFT enhances the model’s performance by providing explicit examples of correct outputs, thereby reducing errors and improving accuracy and robustness.

Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning from Human Feedback (RLHF) is an advanced machine learning technique that incorporates human judgment into the training process of reinforcement learning models. Unlike traditional reinforcement learning, which relies on predefined reward signals, RLHF leverages feedback from human evaluators to guide the model’s behavior. This approach is especially useful for complex or subjective tasks where it is challenging to define a reward function programmatically. Human feedback is collected, often by having humans evaluate the model’s outputs and provide scores or preferences. This feedback is then used to update the model’s reward function, aligning it more closely with human values and expectations. The model is fine-tuned based on this updated reward function, iteratively improving its performance according to human-provided criteria. RLHF helps produce models that are technically proficient and aligned with human values and ethical considerations, making them more reliable and trustworthy in real-world applications.

Q27. What is PEFT LoRA in Fine tuning? 

Answer: Parameter efficient fine-tuning (PEFT) is a method that reduces the number of trainable parameters needed to adapt a large pre-trained model to specific downstream applications. PEFT significantly decreases computational resources and memory storage needed to yield an effectively fine-tuned model, making it more stable than full fine-tuning methods, particularly for Natural Language Processing (NLP) use cases.

Partial fine-tuning, also known as selective fine-tuning, aims to reduce computational demands by updating only the select subset of pre-trained parameters most critical to model performance on relevant downstream tasks. The remaining parameters are “frozen,” ensuring they will not be changed. Some partial fine-tuning methods include updating only the layer-wide bias terms of the model and sparse fine-tuning methods that update only a select subset of overall weights throughout the model.

Additive fine-tuning adds extra parameters or layers to the model, freezes the existing pre-trained weights, and trains only those new components. This approach helps retain stability of the model by ensuring that the original pre-trained weights remain unchanged. While this can increase training time, it significantly reduces memory requirements because there are far fewer gradients and optimization states to store. Further memory savings can be achieved through quantization of the frozen model weights.

Adapters inject new, task-specific layers added to the neural network and train these adapter modules in lieu of fine-tuning any of the pre-trained model weights. Reparameterization-based methods like Low Rank Adaptation (LoRA) leverage low-rank transformation of high-dimensional matrices to capture the underlying low-dimensional structure of model weights, greatly reducing the number of trainable parameters. LoRA eschews direct optimization of the matrix of model weights and instead optimizes a matrix of updates to model weights (or delta weights), which is inserted into the model.

Q28. When to use Prompt Engineering or  RAG or Fine Tuning? 

Answer: Prompt Engineering: Used when you have a small amount of static data and need quick, straightforward integration without modifying the model. It is suitable for tasks with fixed information and when context windows are sufficient.

Retrieval Augmented Generation (RAG): Ideal when you need the model to generate responses based on dynamic or frequently updated data. Use RAG if the model must provide grounded, citation-based outputs.

Fine-Tuning: Choose this when specific, well-defined tasks require the model to learn from input-output pairs or human feedback. Fine-tuning is beneficial for personalized tasks, classification, or when the model’s behavior needs significant customization.

Generative AI Interview Questions Related to SLMs

Q29. What are SLMs (Small Language Models)?

Answer: SLMs are essentially smaller versions of their LLM counterparts. They have significantly fewer parameters, typically ranging from a few million to a few billion, compared to LLMs with hundreds of billions or even trillions. This differ

• Efficiency: SLMs require less computational power and memory, making them suitable for deployment on smaller devices or even edge computing scenarios. This opens up opportunities for real-world applications like on-device chatbots and personalized mobile assistants.
• Accessibility: With lower resource requirements, SLMs are more accessible to a broader range of developers and organizations. This democratizes AI, allowing smaller teams and individual researchers to explore the power of language models without significant infrastructure investments.
• Customization: SLMs are easier to fine-tune for specific domains and tasks. This enables the creation of specialized models tailored to niche applications, leading to higher performance and accuracy.

Q30. How do SLMs work?

Answer: Like LLMs, SLMs are trained on massive datasets of text and code. However, several techniques are employed to achieve their smaller size and efficiency:

• Knowledge Distillation: This involves transferring knowledge from a pre-trained LLM to a smaller model, capturing its core capabilities without the full complexity.
• Pruning and Quantization: These techniques remove unnecessary parts of the model and reduce the precision of its weights, respectively, further reducing its size and resource requirements.
• Efficient Architectures: Researchers are continually developing novel architectures specifically designed for SLMs, focusing on optimizing both performance and efficiency.

Q31. Mention some examples of small language models?

Answer: Here are some examples of SLMs:

• GPT-2 Small: OpenAI’s GPT-2 Small model has 117 million parameters, which is considered small compared to its larger counterparts, such as GPT-2 Medium (345 million parameters) and GPT-2 Large (774 million parameters). Click here
• DistilBERT: DistilBERT is a distilled version of BERT (Bidirectional Encoder Representations from Transformers) that retains 95% of BERT’s performance while being 40% smaller and 60% faster. DistilBERT has around 66 million parameters.
• TinyBERT: Another compressed version of BERT, TinyBERT is even smaller than DistilBERT, with around 15 million parameters. Click here

While SLMs typically have a few hundred million parameters,  some larger models with 1-3 billion parameters can also be classified as SLMs because they can still be run on standard GPU hardware. Here are some of the examples of such models:

• Phi3 Mini: Phi-3-mini is a compact language model with 3.8 billion parameters, trained on a vast dataset of 3.3 trillion tokens. Despite its smaller size, it competes with larger models like Mixtral 8x7B and GPT-3.5, achieving notable scores of 69% on MMLU and 8.38 on MT-bench. Click here.
• Google Gemma 2B: Google Gemma 2B is a part of the Gemma family, lightweight open models designed for various text generation tasks. With a context length of 8192 tokens, Gemma models are suitable for deployment in resource-limited environments like laptops, desktops, or cloud infrastructures.
• Databricks Dolly 3B: Databricks’ dolly-v2-3b is a commercial-grade instruction-following large language model trained on the Databricks platform. Derived from pythia-2.8b, it’s trained on around 15k instruction/response pairs covering various domains. While not state-of-the-art, it exhibits surprisingly high-quality instruction-following behavior. Click here.

Q32. What are the benefits and drawbacks of SLMs?

Answer: One benefit of Small Language Models (SLMs) is that they may be trained on relatively small datasets. Their low size makes deployment on mobile devices easier, and their streamlined structures improve interpretability.

The capacity of SLMs to process data locally is a noteworthy advantage, which makes them especially useful for Internet of Things (IoT) edge devices and businesses subject to strict privacy and security requirements.

However, there is a trade-off when using small language models. SLMs have more limited knowledge bases than their Large Language Model (LLM) counterparts because they were trained on smaller datasets. Furthermore, compared to larger models, their comprehension of language and context is typically more restricted, which could lead to less precise and nuanced responses.

Generative AI Interview Questions Related to Difussion

Q33. What is a diffusion model?

Answer: The idea of the diffusion model is not that old. In the 2015 paper called “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”, the Authors described it like this:

The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data.

The diffusion process is split into forward and reverse diffusion processes. The forward diffusion process turns an image into noise, and the reverse diffusion process is supposed to turn that noise into the image again. 

Q34. What is the forward diffusion process?

Answer: The forward diffusion process is a Markov chain that starts from the original data x and ends at a noise sample ε. At each step t, the data is corrupted by adding Gaussian noise to it. The noise level increases as t increases until it reaches 1 at the final step T.

Q35. What is the reverse diffusion process?

Answer: The reverse diffusion process aims to convert pure noise into a clean image by iteratively removing noise. Training a diffusion model is to learn the reverse diffusion process to reconstruct an image from pure noise. If you guys are familiar with GANs, we’re trying to train our generator network, but the only difference is that the diffusion network does an easier job because it doesn’t have to do all the work in one step. Instead, it uses multiple steps to remove noise at a time, which is more efficient and easy to train, as figured out by the authors of this paper. 

Q36. What is the noise schedule in the diffusion process?

Answer: The noise schedule is a critical component in diffusion models, determining how noise is added during the forward process and removed during the reverse process. It defines the rate at which information is destroyed and reconstructed, significantly impacting the model’s performance and the quality of generated samples.

A well-designed noise schedule balances the trade-off between generation quality and computational efficiency. Too rapid noise addition can lead to information loss and poor reconstruction, while too slow a schedule can result in unnecessarily long computation times. Advanced techniques like cosine schedules can optimize this process, allowing for faster sampling without sacrificing output quality. The noise schedule also influences the model’s ability to capture different levels of detail, from coarse structures to fine textures, making it a key factor in achieving high-fidelity generations.

Q37. What are Multimodal LLMs?

Answer: Advanced artificial intelligence (AI) systems known as multimodal large language models (LLMs) can interpret and produce various data types, including text, images, and even audio. These sophisticated models combine natural language processing with computer vision and occasionally audio processing capabilities, unlike standard LLMs that only concentrate on text. Their adaptability enables them to carry out various tasks, including text-to-image generation, cross-modal retrieval, visual question answering, and image captioning.

The primary benefit of multimodal LLMs is their capacity to comprehend and integrate data from diverse sources, offering more context and more thorough findings. The potential of these systems is demonstrated by examples such as DALL-E and GPT-4 (which can process images). Multimodal LLMs do, however, have certain drawbacks, such as the demand for more complicated training data, higher processing costs, and possible ethical issues with synthesizing or modifying multimedia content. Notwithstanding these difficulties, multimodal LLMs mark a substantial advancement in AI’s capacity to engage with and comprehend the universe in methods that more nearly resemble human perception and thought processes.

MCQs on Generative AI

MCQs on Generative AI Related to Transformers

Q38. What is the primary advantage of the transformer architecture over RNNs and LSTMs?

A. Better handling of long-range dependencies

B. Lower computational cost

C. Smaller model size

D. Easier to interpret

Answer: A. Better handling of long-range dependencies

Q39. In a transformer model, what mechanism allows the model to weigh the importance of different words in a sentence?

A. Convolution

B. Recurrence

C. Attention

D. Pooling

Answer: C. Attention

Q40. What is the function of the positional encoding in transformer models?

A. To normalize the inputs

B. To provide information about the position of words

C. To reduce overfitting

D. To increase model complexity

Answer: B. To provide information about the position of words

MCQs on Generative AI Related to Large Language Models (LLMs)

Q41. What is a key characteristic of large language models?

A. They have a fixed vocabulary

B. They are trained on a small amount of data

C. They require significant computational resources

D. They are only suitable for translation tasks

Answer: C. They require significant computational resources

Q42. Which of the following is an example of a large language model?

A. VGG16

B. GPT-4

C. ResNet

D. YOLO

Answer: B. GPT-4

Q42. Why is fine-tuning often necessary for large language models?

A. To reduce their size

B. To adapt them to specific tasks

C. To speed up their training

D. To increase their vocabulary

Answer: B. To adapt them to specific tasks

MCQs on Generative AI Related to Prompt Engineering

Q43. What is the purpose of temperature in prompt engineering?

A. To control the randomness of the model’s output

B. To set the model’s learning rate

C. To initialize the model’s parameters

D. To adjust the model’s input length

Answer: A. To control the randomness of the model’s output

Q44. Which of the following strategies is used in prompt engineering to improve model responses?

A. Zero-shot prompting

B. Few-shot prompting

C. Both A and B

D. None of the above

Answer: C. Both A and B

Q45. What does a higher temperature setting in a language model prompt typically result in?

A. More deterministic output

B. More creative and diverse output

C. Lower computational cost

D. Reduced model accuracy

Answer: B. More creative and diverse output

MCQs on Generative AI Related to Retrieval-Augmented Generation (RAGs)

Q46. What is the primary benefit of using retrieval-augmented generation (RAG) models?

A. Faster training times

B. Lower memory usage

C. Improved generation quality by leveraging external information

D. Simpler model architecture

Answer: C. Improved generation quality by leveraging external information

Q47. In a RAG model, what is the role of the retriever component?

A. To generate the final output

B. To retrieve relevant documents or passages from a database

C. To preprocess the input data

D. To train the language model

Answer: B. To retrieve relevant documents or passages from a database

Q48. What kind of tasks are RAG models particularly useful for?

A. Image classification

B. Text summarization

C. Question answering

D. Speech recognition

Answer: C. Question answering

MCQs on Generative AI Related to Fine-Tuning

Q49. What does fine-tuning a pre-trained model involve?

A. Training from scratch on a new dataset

B. Adjusting the model’s architecture

C. Continuing training on a specific task or dataset

D. Reducing the model’s size

Answer: C. Continuing training on a specific task or dataset

Q50. Why is fine-tuning a pre-trained model often more efficient than training from scratch?

A. It requires less data

B. It requires fewer computational resources

C. It leverages previously learned features

D. All of the above

Answer: D. All of the above

Q51. What is a common challenge when fine-tuning large models?

A. Overfitting

B. Underfitting

C. Lack of computational power

D. Limited model size

Answer: A. Overfitting

MCQs on Generative AI Related to Stable Diffusion

Q52. What is the primary goal of stable diffusion models?

A. To enhance the stability of training deep neural networks

B. To generate high-quality images from text descriptions

C. To compress large models

D. To improve the speed of natural language processing

Answer: B. To generate high-quality images from text descriptions

Q53. In the context of stable diffusion models, what does the term ‘denoising’ refer to?

A. Reducing the noise in input data

B. Iteratively refining the generated image to remove noise

C. Simplifying the model architecture

D. Increasing the noise to improve generalization

Answer: B. Iteratively refining the generated image to remove noise

Q54. Which application is stable diffusion particularly useful for?

A. Image classification

B. Text generation

C. Image generation

D. Speech recognition

Answer: C. Image generation

In this article, we have seen different interview questions on generative AI that can be asked in an interview. Generative AI now spans a lot of industries, from healthcare to entertainment to personal recommendations. With a good understanding of the fundamentals and a strong portfolio, you can extract the full potential of generative AI models. Although the latter comes from practice, I’m sure prepping with these questions will make you thorough for your interview. So, all the very best to you for your upcoming GenAI interview!

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Data science Trainee at Analytics Vidhya, specializing in ML, DL and Gen AI. Dedicated to sharing insights through articles on these subjects. Eager to learn and contribute to the field’s advancements. Passionate about leveraging data to solve complex problems and drive innovation.