DeepMind's Gemma Scope provides researchers with tools to better understand how Gemma 2 language models work through a collection of sparse autoencoders. This helps in understanding the inner workings of these models and addressing concerns like hallucinations and potential manipulation.
An overview of the LIDA library, including how to get started, examples, and considerations going forward, with a focus on large language models (LLMs) and image generation models (IGMs) in data visualization and business intelligence.
Inspectus is a versatile visualization tool for large language models, offering multiple views to provide diverse insights into language model behaviors. It runs in Jupyter notebooks via a Python API and supports visualization of attention maps, token heatmaps, and dimension heatmaps. The library can be installed using pip and provides API documentation and tutorials for Huggingface models and custom attention maps.
A Python-based, open-source visualization tool called Inspectus helps researchers and developers analyze attention patterns in large language models within Jupyter notebooks. It provides an intuitive interface with multiple views, including attention matrices, heatmaps, and dimension heatmaps, to facilitate detailed analysis.
Google has launched Model Explorer, an open-source tool designed to help users navigate and understand complex neural networks. The tool aims to provide a hierarchical approach to AI model visualization, enabling smooth navigation even for massive models. Model Explorer has already proved valuable in the deployment of large models to resource-constrained platforms and is part of Google's broader ‘AI on the Edge’ initiative.
- Challenges in measuring similarity between unstructured text data like movie descriptions.
- Simple NLP methods may not yield meaningful results; thus, a controlled vocabulary is proposed.
- Using an LLM, a genre list is generated for movie titles, which helps improve the similarity model.
A function is created to find the most similar movies to a given title based on cosine similarity scores.
Network visualization highlights clusters of genres linked via movies, showcasing potential improvements in recommender systems.
- Simple NLP methods may not yield meaningful results; thus, a controlled vocabulary is proposed.
- Using an LLM, a genre list is generated for movie titles, which helps improve the similarity model.
A function is created to find the most similar movies to a given title based on cosine similarity scores.
Network visualization highlights clusters of genres linked via movies, showcasing potential improvements in recommender systems.
