LLM Knowledge Sources (January 2024)

English summary

The video explains three main sources of knowledge used by large language models and AI chatbots when constructing answers, along with their training and retrieval processes. It also describes how retrieval-augmented generation and prompt design can be used to improve information recency, relevance, and response format.

Key points

• Chatbot knowledge may come from parametric knowledge acquired through pre-training and fine-tuning, retrieved web or external documents, or context supplied directly by the user in the prompt.
• Pre-training learns statistical next-token probabilities from large text collections and establishes foundations involving grammar, facts, and relationships. Fine-tuning uses smaller, more refined datasets to shape output formats, conversational behavior, and domain-specific response styles.
• In retrieval-augmented generation, documents are divided into smaller units, converted into vector embeddings, and stored. Units relevant to a query are then retrieved and added to the model prompt. This can support the use of up-to-date material without frequent retraining of the core model.

Audience

Researchers, developers, students, and general users seeking to understand how LLMs generate answers and how they can be used in research and professional workflows

Scope and limitations

This summary is based only on the evidence notes supplied from the January 2024 video. The descriptions of GPT-3 training time and cost, named tools, and illustrative cases are not accompanied in the supplied text by independently verified methods or performance evaluations. The examples therefore should not be treated as evidence of general effectiveness or current availability. The limitations stated in the video include the substantial computational and financial burden of updating parametric knowledge, possible dependence of browsing features on paid tiers or dedicated platforms, and the token limits and manual effort associated with directly pasting source text into prompts.

Research context

The topic is thematically connected to medical AI and data science through the design and evaluation of knowledge-intensive AI systems that use external literature retrieval, document-based question answering, retrieval-augmented generation, and prompt engineering.