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Combining knowledge graphs with embeddings to enable multi-hop reasoning and contextual understanding in LLMs, while supporting natural language querying.
Embeddings
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- llms (9)
- ai-engineering (5)
- rag (3)
- ai-safety (3)
- deep-dive (3)
- nlp (3)
- function-calling (2)
- agents (2)
- fine-tuning (2)
- modern-bert (2)
- ai-governance (2)
embeddings (2)
- agentic-ai (1)
- design-patterns (1)
- agents-orchestration (1)
- in-context-learning (1)
- openai (1)
- autogen (1)
- rdi-berkeley (1)
- model-context-protocol (1)
- deep-learning (1)
- multilayer-perceptron (1)
- backpropagation (1)
- gradient-descent (1)
- hugging-face-transformers (1)
- responsible-ai (1)
- artificial-general-intelligence (1)
- fundamental-rights (1)
- knowledge-graphs (1)
- langchain (1)
- neo4j (1)
- semantic-search (1)
- bm25 (1)
- vector-storage (1)
- reranking (1)
- contextual-retrieval (1)
- quantization (1)
- clustering (1)
- topic-modeling (1)
- innovation (1)
- research (1)
- ai-ethics (1)
- hackathon (1)
- conference (1)
- reasoning-models (1)
- scientific-discovery (1)
- ai-risks (1)
- spatial-intelligence (1)
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The ever-growing volume of research publications necessitates efficient methods for structuring this body of knowledge. This solution uses Machine Learning (UMAP, HDBSCAN), Embedding Quantization, and an LLM pipeline to classify 25,000 arXiv publications under a novel taxonomy.