Seven Cool GenAI & LLM Technical Interview Questions

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Translator | Jingyan

Reviewer | Chonglou

is different from the traditional question banks that can be found everywhere on the Internet. These questions require outside-the-box thinking.

Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text and answer questions, engage in conversations with humans, and provide accurate and valuable information. GenAI relies on LLM algorithms and models, which can generate a variety of creative features. However, although GenAI and LLM are becoming more and more common, we still lack detailed resources that can deeply understand their complexity. Newcomers in the workplace often feel like they are stuck in unknown territory when conducting interviews about the functions and practical applications of GenAI and LLM.

To this end, we have compiled this guidebook to record technical interview questions about GenAI & LLM. Complete with in-depth answers, this guide is designed to help you prepare for interviews, approach challenges with confidence, and gain a deeper understanding of the impact and potential of GenAI & LLM in shaping the future of AI and data science.

1. How to build a knowledge graph using an embedded dictionary in Python?

One way is to use a hash (a dictionary in Python, also called a key-value table), where the key (key) is a word, token, concept, or category, such as "mathematics." Each key corresponds to a value, which itself is a hash: a nested hash. The key in the nested hash is also a word related to the parent key in the parent hash, such as a word like "calculus". The value is a weight: "calculus" has a high value because "calculus" and "mathematics" are related and often appear together; conversely, "restaurants" has a low value because "restaurants" and "mathematics" rarely appear together.

In LLM, nested hashing may be embedding (a method of mapping high-dimensional data to a low-dimensional space, usually used to convert discrete, non-continuous data into a continuous vector representation, to facilitate computer processing). Since nested hashing does not have a fixed number of elements, it handles discrete graphs much better than vector databases or matrices. It brings faster algorithms and requires less memory.

2. How to perform hierarchical clustering when the data contains 100 million keywords?

If you want to cluster keywords, then for each pair of keywords {A, B }, you can calculate the similarity between A and B to learn how similar the two words are. The goal is to generate clusters of similar keywords.

Standard Python libraries such as Sklearn provide agglomerative clustering, also known as hierarchical clustering. However, in this example, they typically require a 100 million x 100 million distance matrix. This obviously doesn't work. In practice, random words A and B rarely appear together, so the distance matrix is ​​very discrete. Solutions include using methods suitable for discrete graphs, such as using nested hashes discussed in question 1. One such approach is based on detecting clustering of connected components in the underlying graph.

3. How do you crawl a large repository like Wikipedia to retrieve the underlying structure, not just the individual entries?

These repositories all embed structured elements into web pages , making the content more structured than it appears at first glance. Some structural elements are invisible to the naked eye, such as metadata. Some are visible and also present in the crawled data, such as indexes, related items, breadcrumbs, or categories. You can search these elements individually to build a good knowledge graph or taxonomy. But you may want to write your own crawler from scratch rather than relying on tools like Beautiful Soup. LLMs rich in structural information (such as xLLM) provide better results. Additionally, if your repository does lack any structure, you can extend your scraped data with structures retrieved from external sources. This process is called "structure augmentation".

4. How to enhance LLM embeddings with contextual tokens?

Embeddings are composed of tokens; these are the smallest text elements you can find in any document. You don't necessarily have to have two tokens, like "data" and "science", you can have four tokens: "data^science", "data", "science", and "data~science". The last one represents the discovery of the term “data science”. The first means that both "data" and "science" are found, but in random positions within a given paragraph, rather than in adjacent positions. Such tokens are called multi-tokens or contextual tokens. They provide some nice redundancy, but if you're not careful you can end up with huge embeddings. Solutions include clearing out useless tokens (keep the longest one) and using variable-size embeddings. Contextual content can help reduce LLM illusions.

5. How to implement self-tuning to eliminate many of the problems associated with model evaluation and training?

This applies to systems based on explainable AI, not neural networks black box. Allow the user of the application to select hyperparameters and mark the ones he likes. Use this information to find ideal hyperparameters and set them to default values. This is automated reinforcement learning based on user input. It also allows the user to select his favorite suit based on the desired results, making your application customizable. Within an LLM, performance can be further improved by allowing users to select specific sub-LLMs (e.g. based on search type or category). Adding a relevance score to each item in your output can also help fine-tune your system.

6. How to increase the speed of vector search by several orders of magnitude?

In LLM, using variable-length embeddings greatly reduces the size of embeddings. Therefore, it speeds up searches for similar backend embeddings to those captured in the frontend prompt. However, it may require a different type of database, such as key-value tables. Reducing the token size and embeddings table is another solution: in a trillion-token system, 95% of tokens will never be extracted to answer a prompt. They're just noise, so get rid of them. Using context tokens (see question 4) is another way to store information in a more compact way. Finally, approximate nearest neighbor (ANN) search is performed on the compressed embeddings. The probabilistic version (pANN) can run much faster, see figure below. Finally, use a caching mechanism to store the most frequently accessed embeddings or queries for better real-time performance.

Probabilistic Approximate Nearest Neighbor Search (pANN)

As a rule of thumb, reducing the size of the training set by 50% will give better results. The fitting effect will also be greatly reduced. In LLM, it's better to choose a few good input sources than to search the entire Internet. Having a dedicated LLM for each top-level category, rather than one size fits all, further reduces the number of embeddings: each tip targets a specific sub-LLM, rather than the entire database.

7. What is the ideal loss function to get the best results from your model?

The best solution is to use the model evaluation metric as the loss function. The reason why this is rarely done is that you need a loss function that can be updated very quickly every time a neuron is activated in the neural network. In the context of neural networks, another solution is to calculate the evaluation metric after each epoch and stay on the epoch-generated solution with the best evaluation score, rather than on the epoch-generated solution with the smallest loss.

I am currently working on a system where the evaluation metric and loss function are the same. Not based on neural networks. Initially, my evaluation metric was the multivariate Kolmogorov-Smirnov distance (KS). But without a lot of calculations, it is extremely difficult to perform atomic updates on KS on big data. This makes KS unsuitable as a loss function since you would need billions of atomic updates. But by changing the cumulative distribution function to a probability density function with millions of bins, I was able to come up with a good evaluation metric that also works as a loss function.

Original title: 7 Cool Technical GenAI & LLM Job Interview Questions, author: Vincent Granville

Link: https://www.datasciencecentral.com/7-cool-technical-genai-llm -job-interview-questions/.

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