Summary of Ragviz: Diagnose and Visualize Retrieval-augmented Generation, by Tevin Wang et al.
RAGViz: Diagnose and Visualize Retrieval-Augmented Generation
by Tevin Wang, Jingyuan He, Chenyan Xiong
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed RAGViz tool combines knowledge from domain-specific sources with large language models to improve answer generation. Current systems lack customizable visibility on the context documents and the model’s attentiveness towards such documents. To address this, RAGViz visualizes the attentiveness of generated tokens in retrieved documents, providing token and document-level attention visualization as well as generation comparison upon context document addition or removal. The tool features a user interface, retrieval index, and Large Language Model backbone, making it an open-source toolkit that can be easily hosted with custom embedding models and HuggingFace-supported LLM backbones. With a median query time of about 5 seconds on moderate GPU nodes, RAGViz operates efficiently. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary RAGViz is a new tool that helps computers better understand what they’re reading. It combines information from different sources to improve the answers it gives. Right now, these systems don’t show how well they’re paying attention to the context of the text. RAGViz changes this by visualizing how well it’s understanding the text and comparing its answers when you add or remove parts of the text. The tool is easy to use and works quickly, making it a powerful new way to understand computers’ abilities. |
Keywords
» Artificial intelligence » Attention » Embedding » Large language model » Token