Top 5 LLM topics




I read 📖 a lot of posts on LinkedIn and Medium about LLMs. I distilled the top 5 topics of discussions from my notes of the last two months. Here are the 5 topics.


1️⃣ RAG (Retrieval-Augmented Generation) techniques and improvements: Research topics include comparing RAG with fine-tuning methods, exploring advanced RAG techniques, multi-hop and agentic RAG architectures, and ways to improve the performance of retrieval-augmented models.

2️⃣ Use of LLMs in enterprise and government: This covers the application of generative AI within city governments, potential use cases for generative AI in organizations, ethics and governance in AI health applications, and the state of generative AI in enterprise settings.

3️⃣ Embeddings and their role in enhancing retrieval: This includes innovative techniques such as Matryoshka Embeddings, improving retrieval mechanisms through strategic inclusion of relevant and noisy documents, and blending models for improved performance.

4️⃣ Agents and their interaction with Knowledge Graphs (KGs): Topics discussed involve using knowledge graphs for reasoning, clustering for knowledge graph creation, leveraging LLMs for tasks related to graphs like fact extraction and resolution, and making KGs from text using different methodologies such as prompting or ontology inclusion.

5️⃣ Ethical considerations and governance of AI: The importance of considering ethics in AI development, transparency in AI applications, particularly in healthcare, and the broader implications of AI-driven decision making processes in legal and business contexts.

What are your top 5 LLM topics?

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