Abstract

LLMs are crucial for efficient deployment and integration in information retrieval and conversational
AI applications. LLMs like GPT-3 and BERT use Transformer architecture and self-attention
mechanisms, allowing sophisticated context-aware representations. Pre-trained on massive data,
LLMs are fine-tuned for specific tasks.

For further to discuss, currently there are three LLM-based approaches which are retrieval-augmented
generation (RAG), semantic search, and conversational memory buffer. RAG combines LLMs with a
retriever component, utilizing external knowledge sources for generating knowledgeable responses. It
is ideal for question-answering and open-domain dialogue but relies on static knowledge. Semantic
search focuses on capturing meaning behind user queries with LLM-generated contextualized
embeddings, improving information retrieval in document ranking and recommendations.
Conversational memory buffer introduces persistent memory into LLM architecture for better
response quality in multi-turn dialogue systems.

Future LLM developments include dynamic knowledge injection for real-time updates, multi-modal
LLMs integrating various data types for richer context, hybrid LLMs combining rule-based systems
and symbolic reasoning techniques, and enhanced evaluation metrics and benchmarks for LLM-
related tasks.

Speaker

siva naga prasad

Timing

Starts at Saturday June 22 2024, 11:30 AM. The sessions runs for about 1 hour.

Resources