Summary of Unsupervised Real-time Hallucination Detection Based on the Internal States Of Large Language Models, by Weihang Su et al.
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
by Weihang Su, Changyue Wang, Qingyao Ai, Yiran HU, Zhijing Wu, Yujia Zhou, Yiqun Liu
First submitted to arxiv on: 11 Mar 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 Large language models (LLMs) can produce coherent yet factually inaccurate responses, a phenomenon known as hallucinations, which hinders their effectiveness in practical applications. To mitigate this issue, researchers have primarily focused on post-processing techniques for detecting hallucinations, which are computationally intensive and limited in effectiveness due to their separation from the LLM’s inference process. This paper introduces MIND, an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. Additionally, it presents HELM, a new benchmark for evaluating hallucination detection across multiple LLMs, featuring diverse LLM outputs and internal states during their inference process. The experiments demonstrate that MIND outperforms existing state-of-the-art methods in hallucination detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can sometimes make things up! This is called hallucinations, and it’s a problem because the model isn’t telling the truth even if its answer sounds smart. To fix this, researchers are working on new ways to detect when the model is making something up. In this paper, they introduce two new tools: MIND and HELM. MIND is a way to train language models so they can catch their own mistakes in real-time. HELM is a special test that helps us figure out which methods work best for detecting hallucinations. By using these tools, we can make sure language models are telling the truth when it counts. |
Keywords
» Artificial intelligence » Hallucination » Inference » Unsupervised
