Summary of Implementation and Application Of An Intelligibility Protocol For Interaction with An Llm, by Ashwin Srinivasan et al.
Implementation and Application of an Intelligibility Protocol for Interaction with an LLM
by Ashwin Srinivasan, Karan Bania, Shreyas V, Harshvardhan Mestha, Sidong Liu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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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 This paper explores the development of interactive systems that combine a machine learning engine with human expertise for data analysis tasks. The focus is on complex problems that don’t lend themselves to traditional statistical or mathematical modeling, such as those found in science, environmental, and medical applications. By harnessing human creativity and pattern recognition capabilities alongside modern machine learning techniques, the authors propose constructing internal representations of data to uncover potential solutions. They present a protocol called , which enables two-way intelligibility between agents capable of making predictions and providing explanations. The authors examine the implementation of this protocol for cases where one agent acts as a generator using a large language model (LLM) and the other is a tester, either human or proxying human expertise. They describe an algorithmic general-purpose implementation and conduct preliminary experiments in radiology and drug-discovery areas, providing early evidence supporting the protocol’s ability to capture one- and two-way intelligibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how humans and machines can work together to solve complex problems. When we try to analyze big data or make sense of complicated science, math, or medicine, it’s hard for computers alone to figure things out. But if humans help machines learn by looking at patterns and making connections, we might get closer to solving these tough problems. The authors propose a way for machines and humans to work together called , which lets them share ideas and insights. They show how this can be done using special algorithms and test it in two areas: medical imaging and finding new medicines. |
Keywords
» Artificial intelligence » Large language model » Machine learning » Pattern recognition
