Summary of Metacheckgpt — a Multi-task Hallucination Detector Using Llm Uncertainty and Meta-models, by Rahul Mehta et al.
MetaCheckGPT – A Multi-task Hallucination Detector Using LLM Uncertainty and Meta-models
by Rahul Mehta, Andrew Hoblitzell, Jack O’Keefe, Hyeju Jang, Vasudeva Varma
First submitted to arxiv on: 10 Apr 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 The SHROOM shared-task aims to tackle hallucinations in large language models (LLMs), a pressing issue in the field. Our team proposes a meta-regressor framework for evaluating and integrating LLMs, achieving top scores on the leaderboard. We also explore various transformer-based models and black box methods like ChatGPT and Vectara. In addition, we conduct an error analysis comparing GPT4 to our best model, highlighting its limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have been creating fake text, which is a big problem. Researchers are trying to solve this issue by working together on the SHROOM task. Our team came up with a new way to evaluate and combine LLMs, making it one of the best in two different parts of the challenge. We also tested some popular models like ChatGPT and Vectara. Finally, we compared our best model to GPT4, showing that it’s not perfect. |
Keywords
» Artificial intelligence » Transformer
