💡 How important are learning paths for gaining the skills needed to tackle real-life problems? 🔬Researchers from the University of Siegen (Germany) and Keio…

We’re excited to launch a huge feature making LlamaIndex the framework for building knowledge graphs with LLMs: The Property Graph Index 💫 (There’s a lot of… | 57 comments on LinkedIn

Catch Thomas Smoker of WhyHow.AI talking with Demetrios Brinkmann of MLOps Community about "Managing Small Knowledge Graphs for Multi-agent Systems" Key…

In applications such as personal assistants, large language models (LLMs) must consider the user's personal information and preferences. However, LLMs lack the inherent ability to learn from user...

To Graph or Not to Graph Knowledge Graph Architectures and LLMs - Download as a PDF or view online for free

Reduce LLM hallucinations with RAG over textual as well as structured knowledge bases. Today we are releasing WSTaRK h, a large-scale LLM retrieval benchmark… | 22 comments on LinkedIn

Graphlit is an API-first platform for developers building AI-powered applications with unstructured data, which leverage domain knowledge in any vertical market such as legal, sales, entertainment, healthcare or engineering.

How can we further increase the accuracy of LLM-powered question answering systems? Ontologies to the rescue! That is the conclusion of the latest research… | 16 comments on LinkedIn

Harnessing Knowledge Graphs to Mitigate Hallucinations in Large Language Models 🏮 Large language models (LLMs) have emerged as powerful tools capable of…

In the realm of artificial intelligence, the fusion of retrieval and generation models has led to significant advancements in natural…

A Joint Project with Nobel Prize Outreach (NPO)

Super cool to see how Gemini uses Graph RAG to create travel plans. Check out this 60 second video. Graphs are everywhere. Emil Eifrem Alyson Welch Chandra…

A Knowledge Graph Forum 2023 presentation by Krasimira Bozhanova, Solutions Architect at Ontotext.

Watch my colleague Jonathan Larson present on GraphRAG!GraphRAG is a research project from Microsoft exploring the use of knowledge graphs and large language...

Learn how to create knowledge graphs easily by turning PDF documents into graph models using LlamaParse for better RAG applications.

Had a great time at The Knowledge Graph Conference last week! Here are my takeaways: Not surprisingly, there was a ton of presentations and talk about GenAI…

While LLMs are adept at pattern recognition and generating human-like text, they often struggle with maintaining accuracy over time and…

If powerful LLMs were all that was needed to get to great enterprise generative AI programs, then hundreds of thousands of open-source and closed-source LLMs… | 46 comments on LinkedIn

An open-source library for building knowledge graphs from text corpus using open-source LLMs like Llama 3 and Mixtral.

With the rise of LLMs, we can now store both structure and semantic meaning as vector embeddings in knowledge graphs.

Global Reasoning with Knowledge Graphs and Generative AI ⏺ Large language models (LLMs) open in a new era of natural language processing…

Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora ... 👉A New Approach to Query-Focused Summarization based on Knowledge… | 21 comments on LinkedIn

DoorDash leverages Large Language Models to build a product knowledge graph that captures relationships between millions of consumer goods.

When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.

Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tackle this problem by leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee that the model utilizes a relevant piece of knowledge from the KG. To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG. Specifically, our SURGE framework first retrieves the relevant subgraph from the KG, and then enforces consistency across facts by perturbing their word embeddings conditioned by the retrieved subgraph. Then, we utilize contrastive learning to ensure that the generated texts have high similarity to the retrieved subgraphs. We validate our SURGE framework on OpendialKG and KOMODIS datasets, showing that it generates high-quality dialogues that faithfully reflect the knowledge from KG.

AI has been a part of healthcare and life sciences for decades. You might remember hearing about the very first chatbot, ELIZA, created at MIT in 1964 by Joseph Weizenbaum to explore communication between machines and humans.

A survey of the current methods of integration

cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs! GitHub…

Transform plain text into a visually stunning Knowledge Graph with GPT-4 (latest preview)! It converts text into RDF tuples, and highlights the most frequent connections with a vibrant color-coding...

Recent work demonstrated great promise in the idea of orchestrating collaborations between LLMs, human input, and various tools to address the inherent limitations of LLMs. We propose a novel perspective called semantic decoding, which frames these collaborative processes as optimization procedures in semantic space. Specifically, we conceptualize LLMs as semantic processors that manipulate meaningful pieces of information that we call semantic tokens (known thoughts). LLMs are among a large pool of other semantic processors, including humans and tools, such as search engines or code executors. Collectively, semantic processors engage in dynamic exchanges of semantic tokens to progressively construct high-utility outputs. We refer to these orchestrated interactions among semantic processors, optimizing and searching in semantic space, as semantic decoding algorithms. This concept draws a direct parallel to the well-studied problem of syntactic decoding, which involves crafting algorithms to best exploit auto-regressive language models for extracting high-utility sequences of syntactic tokens. By focusing on the semantic level and disregarding syntactic details, we gain a fresh perspective on the engineering of AI systems, enabling us to imagine systems with much greater complexity and capabilities. In this position paper, we formalize the transition from syntactic to semantic tokens as well as the analogy between syntactic and semantic decoding. Subsequently, we explore the possibilities of optimizing within the space of semantic tokens via semantic decoding algorithms. We conclude with a list of research opportunities and questions arising from this fresh perspective. The semantic decoding perspective offers a powerful abstraction for search and optimization directly in the space of meaningful concepts, with semantic tokens as the fundamental units of a new type of computation.