Summary of Hallucination Detection in Foundation Models For Decision-making: a Flexible Definition and Review Of the State Of the Art, by Neeloy Chakraborty and Melkior Ornik and Katherine Driggs-campbell
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
by Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Robotics (cs.RO)
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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 limitations of autonomous systems that rely on modular sub-components for decision-making, planning, and control. While these systems perform well within their designed scenarios, they struggle to adapt to out-of-distribution situations. Foundation models trained on multiple tasks have shown promise in bridging this gap, but they are prone to hallucination and generating poor decisions. The authors argue that it is crucial to develop systems that can quantify the certainty of a model’s decision and detect when it may be hallucinating. To achieve this, the paper discusses current use cases for foundation models, defines hallucinations with examples, reviews existing approaches to detection and mitigation, and provides guidelines and areas for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous systems are getting smarter! They’re used in industries like manufacturing, healthcare, and entertainment. But sometimes they make mistakes because they weren’t trained for certain situations. Researchers think that big models trained on lots of data can help fix this problem. These models are good at making decisions, but they can also make up things that aren’t true (this is called hallucination). The authors want to find a way to stop these systems from making mistakes and make sure they’re making good choices. They discuss how foundation models work, what happens when they get confused, and ways to fix the problem. |
Keywords
» Artificial intelligence » Hallucination
