Adapting Foundation Models

Question 1

What is adaptation in the context of foundation models?

Answer 1

The process of customizing pre-trained models to perform better in specific tasks or domains by re-training them with new data.

Question 2

Why is adaptation important?

Answer 2

It enhances performance in specialized tasks, aligns outputs with organizational needs, and respects privacy constraints.

Question 3

Give an example of domain-specific adaptation.

Answer 3

A chatbot for banking fine-tuned to answer financial questions about loans and accounts.

Question 4

How can private data be used in adaptation?

Answer 4

Fine-tuning a model with an organization’s internal data improves relevance while maintaining privacy.

Question 5

What is instruction-based adaptation?

Answer 5

Fine-tuning models using instructional data (e.g., manuals) to improve their performance on unseen tasks.

Question 6

What is RAG?

Answer 6

RAG stands for Retrieval-Augmented Generation, a framework combining retrieval of external data with natural language generation.

Question 7

Why is RAG useful?

Answer 7

It overcomes knowledge cutoffs, improves accuracy, reduces hallucinations, and enables domain adaptability.

Question 8

What are the main components of RAG?

Answer 8

Components of a RAG System
- Retriever:
- - Fetches relevant information from external sources.
- - Techniques:
- - - Sparse Retrieval: BM25, TF-IDF.
- - - Dense Retrieval: Embeddings via transformers like BERT.
- - Tools: Elasticsearch, FAISS, Pinecone.

• Generator:
• • A language model that processes the retrieved information and generates the output.
• • Common Models: GPT, T5, BART.
• Knowledge Base:
• • The external data source where the retriever looks for information.
• • Examples: Databases, Wikipedia, proprietary datasets.

Question 9

Give an example of a RAG application.

Answer 9

Customer support systems retrieving answers from a company knowledge base.

Question 10

What is a common challenge in RAG systems?

Answer 10

Retriever Quality:
Poor retrieval mechanisms can provide irrelevant or incomplete data.
Latency:
Real-time retrieval and generation can be slower compared to static responses from an LLM.
Integration Complexity:
Combining retrievers, generators, and external databases requires careful engineering.