- Published on
Digital scams cause devastating impacts across society. MINERVA is an AutoGen implementation of seven agents that helps users identify scam attempts, achieving higher accuracy than baseline prompting methods (88.3% vs. 69.5%).
Llms
All Posts
- 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)
- Published on
Combining knowledge graphs with embeddings to enable multi-hop reasoning and contextual understanding in LLMs, while supporting natural language querying.- Published on
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.- Published on