Summary of Glimpse: Enabling White-box Methods to Use Proprietary Models For Zero-shot Llm-generated Text Detection, by Guangsheng Bao et al.
Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection
by Guangsheng Bao, Yanbin Zhao, Juncai He, Yue Zhang
First submitted to arxiv on: 16 Dec 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 proposed Glimpse approach estimates the full predictive distributions from partial observations, enabling white-box methods like Entropy, Rank, Log-Rank, and Fast-DetectGPT to use proprietary models. This is achieved by extending these methods to latest proprietary models, such as GPT-3.5, which improves the average AUROC score by 51% relative to an open-source baseline. The authors demonstrate that advanced LLMs can effectively detect their own outputs, suggesting a potential shield against themselves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach called Glimpse to detect large language model-generated text. It helps white-box methods use proprietary models, which is important because current methods have limitations. The results show that this approach works well and can even help detect text generated by advanced models like GPT-3.5. |
Keywords
» Artificial intelligence » Gpt » Large language model
