Summary of Hallucination Detection in Llms: Fast and Memory-efficient Fine-tuned Models, by Gabriel Y. Arteaga et al.
Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
by Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 addresses the critical issue of uncertainty estimation in high-risk applications like autonomous vehicles, medicine, or insurance. Large Language Models (LLMs) have gained popularity but are prone to hallucinations, which can lead to serious harm. Despite their success, LLMs require substantial computational resources and memory, making it impractical to use ensembling methods. The proposed method enables fast and efficient training of LLM ensembles using a single GPU for both training and inference. The resulting ensembles can effectively detect hallucinations, providing a viable solution for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make AI safer by solving the problem of uncertainty estimation in high-risk settings like self-driving cars or medical diagnosis. Currently, Large Language Models (LLMs) are popular but can sometimes “make up” things that aren’t true, which could be disastrous. LLMs take a lot of computing power and memory to train and use, making it hard to combine multiple models for better results. The new method in this paper lets you train and use many LLMs quickly and efficiently using just one computer. This helps prevent these “imagination” problems and makes AI more reliable. |
Keywords
» Artificial intelligence » Inference
