Neo4J as Vector Store with Langchain, OpenAI, and Wikipedia Español
Aprende a utilizar bases de datos gráficas con Langchain, Neo4J y OpenAI para crear un almacén de vectores usando Wikipedia en español.
Content Curation in Online Platforms - Impact and Improvement Strategies
Explore the impact of content curation on online platforms, examining moderation practices, user behavior, and the potential of academic research to guide policy and improve digital experiences.
Retrieving Complex Answers through Knowledge Graph and Text
Explore algorithms for retrieving complex answers by combining knowledge graphs and text. Learn techniques to identify relevant entities, relations, and explanatory passages for comprehensive query responses.
Retrieving Complex Answers through Knowledge Graph and Text
Explore techniques for retrieving complex answers by combining knowledge graphs and text. Learn about algorithms that identify relevant entities, relations, and supporting passages for comprehensive query responses.
Informath: Informalization of Formal Mathematics
Exploring methods to bridge formal mathematics and everyday mathematical language, aiming to make advanced concepts more accessible and enhance AI's ability to construct formal proofs.
Can Wikipedia Help Offline Reinforcement Learning - Author Interview
Exploring the potential of pre-trained language models in offline reinforcement learning, discussing experimental results, model performance, and future directions for leveraging sequence modeling techniques in RL tasks.
Non-English Editions of Wikipedia Have a Misinformation Problem
Explore the challenges of misinformation in non-English Wikipedia editions and strategies to combat this growing issue in global information sharing.
Prompting Language Models Improves Quoting from Pre-Training Data - EACL 2024
Explore how "according-to" prompting enhances LLMs' ability to ground responses in factual data, reducing hallucinations and improving task performance across diverse corpora.
Reading Politics of the Supposedly Neutral
Explore how algorithms and technological systems embody political ideologies and class interests, examining predator-prey models, labor supply formulas, and Wikipedia's potential for monetization.
How a Ragtag Band Created Wikipedia
Discover the origins of Wikipedia, its collaborative nature, and the principles that guide this revolutionary online encyclopedia in this insightful talk by its founder.
Are Anonymity-Seekers Just like Everybody Else? An Analysis of Contributions to Wikipedia from Tor
Análisis de contribuciones anónimas a Wikipedia a través de Tor, comparando calidad y patrones con otros tipos de usuarios para evaluar el impacto de bloquear proxies de privacidad.
Entbehrliches - Unterhaltsames Wissen aus der Wikipedia
Entdecken Sie kuriose Wikipedia-Artikel: Zwei Experten präsentieren und diskutieren über 600 amüsante, interessante und skurrile Einträge aus der Online-Enzyklopädie.
Entbehrliches - Unterhaltsames Wissen aus der Wikipedia
Unterhaltsame Präsentation skurriler Wikipedia-Artikel: Zwei Moderatoren stellen kuriose Einträge vor und diskutieren darüber, begleitet von einem Gast. Amüsanter Einblick in ungewöhnliches Wissen.
Semantic Search through the Complete Wikipedia with Weaviate’s GraphQL API
Explore semantic search capabilities using Weaviate's GraphQL API to navigate the entire Wikipedia dataset, enhancing information retrieval and knowledge discovery.
Way-New Collaboration
Explore the evolution of human collaboration, from hunting mastodons to Wikipedia, and its impact on media, collective action, and our future.
Online Safety and How to Protect Our Open Movement
Explore online safety strategies and safeguarding open movements with Wikipedia co-founder Jimmy Wales in this thought-provoking keynote address.
Neo4J as Vector Store with Langchain, OpenAI, and Wikipedia
Explore Neo4J as a vector store using Langchain, OpenAI embeddings, and Wikipedia data for advanced data science and machine learning applications.
LLM Projects - A Quick Tutorial on Multi-Agent Workflows with AutoGen
Explore multi-agent workflows using AutoGen framework for RAG-based question answering with Wikipedia integration. Learn to implement and leverage this powerful approach for dynamic information retrieval.
Fine-tuning Llama 3 on Wikipedia Datasets for Low-Resource Languages
Learn to fine-tune Llama 3 on Wikipedia datasets for low-resource languages. Master dataset creation, LoRA setup, blending techniques, and parameter optimization for improved language model performance.
Building a Scalable AI Chatbot with Wikipedia Data - Semantic Search and RAG
Learn to create an AI chatbot using Wikipedia video game data, implementing semantic search and RAG with SingleStore, optimizing with vector indexes, and integrating GPT models for an interactive experience.
Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data
Explore techniques to enhance LLM factual consistency using out-of-domain data and two-step fine-tuning. Learn to leverage Weights & Biases for model evaluation and improve hallucination detection.