Summary of Your Finetuned Large Language Model Is Already a Powerful Out-of-distribution Detector, by Andi Zhang et al.
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
by Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 re-examines the likelihood ratio between a large language model (LLM) and its finetuned variant as an out-of-distribution (OOD) detection criterion. The idea is that the pretrained LLM has prior knowledge about OOD data due to its vast training dataset, and after finetuning with in-distribution data, it can effectively distinguish between them. Leveraging the power of LLMs, the authors show that this likelihood ratio serves as an effective OOD detection criterion. They also apply this approach to detect OOD questions in question-answering (QA) systems, which can improve specialized LLM performance for general questions. Given that likelihood values are easily obtainable from loss functions within neural network frameworks, implementation is straightforward. The authors demonstrate the effectiveness of their method through comprehensive evaluations across various settings, including far OOD, near OOD, spam detection, and QA scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a closer look at how to detect when something is outside the norm. It uses large language models (LLMs) that have been trained on lots of text data. The idea is that these LLMs can recognize patterns in normal data, but will struggle with data that’s really different. The authors show that by comparing the original model to a version that’s been fine-tuned for specific tasks, they can detect when something is outside what the model has seen before. This could be useful for improving chatbots or question-answering systems. Overall, this method is easy to use and works well in lots of different situations. |
Keywords
» Artificial intelligence » Large language model » Likelihood » Neural network » Question answering
