Explore >> Select a destination
|
You are here 
|
blog.acolyer.org | ||
| | | | |
www.shaped.ai
|
|
| | | | | This article explores how cross-encoders, long praised for their performance in neural ranking, may in fact be reimplementing classic information retrieval logic, specifically, a semantic variant of BM25. Through mechanistic interpretability techniques, the authors uncover circuits within MiniLM that correspond to term frequency, IDF, length normalization, and final relevance scoring. The findings bridge modern transformer-based relevance modeling with foundational IR principles, offering both theoretical insight and a roadmap for building more transparent and interpretable neural retrieval systems. | |
| | | | |
www.mlpowered.com
|
|
| | | | | Blog posts and other information | |
| | | | |
towardsml.wordpress.com
|
|
| | | | | How is Google Translate able to understand text and convert it from one language to another? How do you make a computer understand that in the context of IT Apple is a company and not a fruit? Or how is a Smart-Keypad able to predict the most likely next few words that you are going... | |
| | | | |
www.jamesserra.com
|
|
| | | [AI summary] The article provides an in-depth overview of OpenAI and Large Language Models (LLMs), discussing their architecture, applications, and integration into business processes. It also explores Microsoft's ecosystem of AI tools, including Azure OpenAI Studio, Azure AI Studio, and Microsoft Copilot Studio, highlighting their distinct roles in AI development and deployment. The piece emphasizes the importance of leveraging LLMs for tasks such as natural language processing, content generation, and data analysis, while also addressing the challenges and considerations in implementing these technologies. Additionally, it touches on the use of Retrieval-Augmented Generation (RAG) techniques to enhance the capabilities of LLMs by incorporating external dat... | ||
