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Equinox Elahin

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reacted to singhsidhukuldeep's post with 🔥 15 days ago
Exciting New Tool for Knowledge Graph Extraction from Plain Text! I just came across a groundbreaking new tool called KGGen that's solving a major challenge in the AI world - the scarcity of high-quality knowledge graph data. KGGen is an open-source Python package that leverages language models to extract knowledge graphs (KGs) from plain text. What makes it special is its innovative approach to clustering related entities, which significantly reduces sparsity in the extracted KGs. The technical approach is fascinating: 1. KGGen uses a multi-stage process involving an LLM (GPT-4o in their implementation) to extract entities and relations from source text 2. It aggregates graphs across sources to reduce redundancy 3. Most importantly, it applies iterative LM-based clustering to refine the raw graph The clustering stage is particularly innovative - it identifies which nodes and edges refer to the same underlying entities or concepts. This normalizes variations in tense, plurality, stemming, and capitalization (e.g., "labors" clustered with "labor"). The researchers from Stanford and University of Toronto also introduced MINE (Measure of Information in Nodes and Edges), the first benchmark for evaluating KG extractors. When tested against existing methods like OpenIE and GraphRAG, KGGen outperformed them by up to 18%. For anyone working with knowledge graphs, RAG systems, or KG embeddings, this tool addresses the fundamental challenge of data scarcity that's been holding back progress in graph-based foundation models. The package is available via pip install kg-gen, making it accessible to everyone. This could be a game-changer for knowledge graph applications!
reacted to burtenshaw's post with 🔥 19 days ago
Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain. 🔗 Follow the org for updates https://huggingface.co/agents-course This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers: - why should you use smolagents vs another library? - how to build agents that use code - build multiagents systems - use vision language models for browser use The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric.
reacted to wassemgtk's post with 🔥 19 days ago
# GESAL: Real-Time Adaptation for LLMs We’re excited to unveil **Graph-Enhanced Singular Adaptive Learning (GESAL)**, a framework that lets LLMs like `meta-llama/Llama-3.2-1B` adapt in real time using user feedback. Check out the code and white paper on GitHub! 🔗 **Code**: [https://github.com/writer/AI-Adaptive-Learning-GESAL](https://github.com/writer/AI-Adaptive-Learning-GESAL) --- ## Why GESAL? Static LLMs struggle to adapt without heavy retraining. GESAL solves this with: - **SVF**: Adapts weights via \( W' = U (\Sigma \cdot z) V^T \), using few parameters. - **Graph Memory**: Stores adaptations in nodes for scalability. - **RL**: Updates via \( J(z) = \mathbb{E}[\log \pi_z(y|x) r] \) based on feedback. --- ## How It Works Ask "How many R’s in ‘strawberry’?" If it says "2" and you say "no," GESAL learns to say "3" next time, avoiding repeats. --- ## Try It Built with Hugging Face’s `transformers`: ```bash pip install transformers torch numpy python Adaptive_Learning_(GESAL).py ``` Needs a Hugging Face token for Llama-3.2-1B. --- ## Results GESAL hits 95% accuracy after 5 feedbacks vs. LoRA’s 70%. It’s efficient (~0.5M params) and scalable.
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