Summary of Hallucination Detection and Hallucination Mitigation: An Investigation, by Junliang Luo et al.
Hallucination Detection and Hallucination Mitigation: An Investigation
by Junliang Luo, Tianyu Li, Di Wu, Michael Jenkin, Steve Liu, Gregory Dudek
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers comprehensively review the existing literature on detecting and mitigating hallucinations in large language models (LLMs), such as ChatGPT, Bard, and Llama. Hallucinations refer to incorrect responses generated by these models alongside correct ones. The authors aim to provide a comprehensive reference for engineers and researchers seeking to apply LLMs to real-world tasks. They discuss the current state-of-the-art methods in hallucination detection and mitigation, highlighting both strengths and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are incredibly good at helping us with many tasks like answering questions or generating text. However, they can also make mistakes and give false information. This is called “hallucination.” In this report, scientists review what we know about finding and fixing these errors in big language models. They want to help people who build and use these models understand how to prevent and detect these mistakes. |
Keywords
» Artificial intelligence » Hallucination » Llama
