Summary of Slm Meets Llm: Balancing Latency, Interpretability and Consistency in Hallucination Detection, by Mengya Hu and Rui Xu and Deren Lei and Yaxi Li and Mingyu Wang and Emily Ching and Eslam Kamal and Alex Deng
SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
by Mengya Hu, Rui Xu, Deren Lei, Yaxi Li, Mingyu Wang, Emily Ching, Eslam Kamal, Alex Deng
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes a novel framework to address latency challenges in real-time applications of large language models (LLMs). The framework combines a small language model (SLM) classifier for initial detection with a LLM as constrained reasoner to generate detailed explanations for detected hallucinated content. The study introduces effective prompting techniques that align LLM-generated explanations with SLM decisions, optimizing real-time interpretable hallucination detection and enhancing the overall user experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in using super smart language models in real time. It’s like trying to find a needle in a haystack fast! The authors created a new way to make these models work better for online tasks, like detecting when someone is making up fake information. They use two types of models together: one that quickly checks if something looks suspicious and another that explains why it thinks so. This makes the whole process faster and more understandable. |
Keywords
» Artificial intelligence » Hallucination » Language model » Prompting
