This paper argues that for collective decision-making based on free-form text, embeddings should measure "preferential similarity" rather than "semantic similarity". Existing embeddings capture a coarse preference signal, but fail when this correlation breaks, a problem formalized as an invariance issue.
This content describes building an AI-driven personalization engine in Joomla, leveraging OpenAI's embedding and completion APIs. It tracks user behavior to create interest profiles, enabling personalized content recommendations without requiring expensive platforms or data science teams.
This paper introduces novel approaches for creating high-quality embeddings for logical statements, crucial for training neural networks to efficiently rank choices made by logical reasoners. These methods involve generating anchors with repeated terms, balancing easy, medium, and hard examples for triplet loss training, and periodically emphasizing the hardest examples.
This article analyzes the theoretical limitations inherent in embedding-based retrieval systems. The author presents a critical perspective on the subject.
![[Paper Analysis] On the Theoretical Limitations of Embedding-Based Retrieval (Warning: Rant)](/cdn-cgi/image/width=3840,quality=75,format=webp/https://i3.ytimg.com/vi/zKohTkN0Fyk/hqdefault.jpg)
A nova ferramenta git-semantic permite que equipes construam e compartilhem um índice de busca semântica de suas bases de código via Git, eliminando chamadas de API redundantes e otimizando as consultas do Claude Code. Isso resolve o gargalo de encontrar o código certo para o Claude Code raciocinar, ao gerar e armazenar embeddings de trechos de código em uma branch Git dedicada.
This article introduces Gemini Embedding 2, the first natively multimodal embedding model. It highlights the capability to build advanced applications using this new technology.
O título sugere um conteúdo sobre modelos de embeddings multimodais e rerankers, utilizando a framework Sentence Transformers. Trata-se de um tópico técnico avançado na área de inteligência artificial.
O título sugere um conteúdo sobre a criação rápida de modelos de embedding específicos para um domínio particular. Ele abordaria o desenvolvimento ágil de soluções personalizadas em inteligência artificial.