Summary of Towards Human-level Understanding Of Complex Process Engineering Schematics: a Pedagogical, Introspective Multi-agent Framework For Open-domain Question Answering, by Sagar Srinivas Sakhinana et al.
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering
by Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel multi-agent framework for open-domain question answering (ODQA) tasks using a hierarchical Retrieval Augmented Generation (RAG) architecture. The framework employs introspective and specialized sub-agents using open-source, small-scale multimodal models with the ReAct prompting technique to analyze Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs). This approach aims to deliver superior performance in ODQA tasks while ensuring data privacy, explainability, and cost-effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand complex diagrams used in chemical plants. You can ask a computer questions about these diagrams, but current AI models are limited because they require lots of data, are expensive to use, and don’t provide clear explanations. This paper introduces a new way to analyze diagrams using a combination of small AI models and a special technique called ReAct. The goal is to make it possible for companies to ask questions about their diagrams without sharing sensitive information or breaking the bank. |
Keywords
» Artificial intelligence » Prompting » Question answering » Rag » Retrieval augmented generation
