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An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
💡 How important are learning paths for gaining the skills needed to tackle real-life problems? 🔬Researchers from the University of Siegen (Germany) and Keio…
an approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
·linkedin.com·
An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
KGC24 takeaways
KGC24 takeaways
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…
·linkedin.com·
KGC24 takeaways
Graph-based RAG vs Vector-based RAG
Graph-based RAG vs Vector-based RAG
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
Graph-based RAG
·linkedin.com·
Graph-based RAG vs Vector-based RAG
Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora
Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora
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
Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora
·linkedin.com·
Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
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.
·arxiv.org·
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
LLMs and KGs: BFFs for AI | LinkedIn
LLMs and KGs: BFFs for AI | LinkedIn
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.
·linkedin.com·
LLMs and KGs: BFFs for AI | LinkedIn
cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs!
cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs!
cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs! GitHub…
cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs!
·linkedin.com·
cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs!
GitHub - iAmmarTahir/KnowledgeGraphGPT: 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 system. Download the results as a convenient JSON file for easy integration into your own projects.
GitHub - iAmmarTahir/KnowledgeGraphGPT: 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 system. Download the results as a convenient JSON file for easy integration into your own projects.
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...
·github.com·
GitHub - iAmmarTahir/KnowledgeGraphGPT: 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 system. Download the results as a convenient JSON file for easy integration into your own projects.
The Era of Semantic Decoding
The Era of Semantic Decoding
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.
·arxiv.org·
The Era of Semantic Decoding