AttentionViz: A Global View of Transformer Attention
Visualizes self-attention mechanism in transformers for improved model understanding, enabling analysis of global patterns and expert feedback, with applications in language and vision transformers.
Provides a visualization tool to help researchers better understand the inner workings of transformer models and improve their performance in natural language processing and computer vision tasks.
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Interactive visualization tool that explains how Stable Diffusion transforms text prompts into images, with animations and interactive elements, running locally in users' web browsers.
Provides a user-friendly tool to explain the process behind stable diffusion for creating convincing images from text prompts, making AI more accessible to non-experts.
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Presents a big benchmark for large-scale text-to-SQL parsing, containing 12,751 pairs of text-to-SQL data and 95 databases with a total size of 33.4 GB, highlighting the new challenges of dirty database contents, external knowledge, and SQL efficiency.
Offers a benchmark for text-to-SQL parsing that includes challenges related to large-scale databases, highlighting the need for models to feature database value comprehension and efficient SQL generation.
Governance of the AI, by the AI, and for the AI
This paper analyzes the relationship between AI and governance, addressing two main aspects of this relationship: the governance of AI by humanity, and the governance of humanity by AI.
The paper offers insights into the governance of AI and how it can be wisely governed by humanity to maximize benefits and minimize costs.
Otter: A Multi-Modal Model with In-Context Instruction Tuning
This paper proposes the introduction of instruction tuning into multi-modal models, introduces Otter, a multi-modal model which showcases improved instruction-following ability and in-context learning, and optimizes OpenFlamingo's implementation for researchers.
The paper explains how incorporating instruction tuning into multi-modal models and utilizing Otter can improve instruction-following ability and in-context learning. Additionally, the optimization of OpenFlamingo's implementation for researchers enables easier training and integration into customized pipelines.