5# Interview Questions on Large Language Models (LLMs)
Question: What are some advantages and challenges of large language models (LLMs)?
Understanding LLM Training
To understand the advantages of large language models, first, we need to understand how large language models are trained, look for our blog to understand how LLMs are trained in 3 simple steps
Advantages of using LLMs
- Low or No annotated data
With large language models, we can achieve state-of-the-art performance on a variety of tasks like text classification, Machine translation, question answer, Summarization, etc without much or no training data.
Low annotated data: With pre-trained large language models, you can fine-tune the models on specific tasks or specific domains with low annotated data to achieve higher accuracy instead of starting to build a model from scratch.
No annotated data: Recent advancements in chat-based models like ChatGPT or PaLM can achieve high accuracy on zero-shot or few-shot examples.
2. Generalization
LLMs are trained on vast amounts of data, allowing them to generalize to new and unseen examples. This means that they can perform well on a wide range of tasks, even if they have not been specifically trained for them
3. Fine-tuning flexibility
LLMs can be fine-tuned on any specific task like summarization, classification, NER, etc., and can be finetuned on specific domain data.
4. Human-like generation
New generative AI models like ChatGPT or PaLM can generate human-like responses based on specific instructions, this is very useful in domains like chatbots, writing assistants, and content creation.
5. Agents
New age generative AI models like ChatGPT or PaLM combined with tools like search, API integration, etc can help automate many of the manual tasks.
Challenges of LLM
- Training cost
Training large language models requires large amounts of training data, it’s very computationally expensive (requires a lot of GPUs), and is energy-intensive.
2. Inference cost
There are 2 important ways to use a large language model, self-hosted or using closed-source APIs.
To understand how to estimate the cost of running an LLM in self-hosted or closed-source API, check our blog below:
3. Hallucination
One of the most challenging issues with LLM is hallucination. Some benchmarks can compare hallucinations of the most popular models
Bonus interview question:
One of the most asked interview questions is — What are some of the strategies to 𝗿𝗲𝗱𝘂𝗰𝗲 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 in large language models (LLMs)?
Read the most comprehensive answer in the below blog:
4. Risk and Misuse -Bias
LLM is as good as the data it’s trained on, if training data contains bias the model may exhibit bias in the output.
5. Risk and Misuse — Adversarial Prompting
Adversarial prompting is when someone bypasses safety guardrails and breaks the guiding principles of the model.
6. Adversarial Prompting- Prompt injection
Prompt injection aims to hijack the model output by using clever prompts that change its behavior
7. Adversarial Prompting — Prompt leaking
Prompt leaking is another type of prompt injection where prompt attacks are designed to leak details from the prompt which could contain confidential or proprietary information that was not intended for the public.
8. Adversarial Prompting — Jailbreaking
Some models will avoid responding to unethical instructions but can be bypassed if the request is contextualized cleverly.
9. Explainability
The ability to explain how an LLM was able to generate a specific result is not easy or obvious for users.
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Large Language Models (LLMs) Interview questions series
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