洞見 - Computer Security and Privacy - # Out-of-Distribution Detection using Likelihood Ratio of Pretrained and Finetuned Language Models

Leveraging Pretrained Large Language Models for Effective Out-of-Distribution Detection

Q: How can the proposed likelihood ratio-based OOD detection method be extended to other domains beyond language models, such as computer vision or audio processing?

The likelihood ratio-based OOD detection method proposed in the context of language models can be extended to other domains like computer vision or audio processing by adapting the fundamental principles to suit the specific characteristics of these domains. Here are some ways to extend the method: Feature Extraction: In computer vision, features extracted from images or videos can be used to calculate likelihood ratios between a pretrained model and its finetuned version. The likelihood of the features extracted from the data can serve as a proxy for in-distribution and out-of-distribution samples. Spectrogram Analysis: For audio processing, spectrogram features can be used to compute likelihood ratios. By comparing the likelihood of spectrogram patterns between a base model and a finetuned model, OOD audio samples can be detected effectively. Transfer Learning: Similar to language models, pretrained models in computer vision or audio processing can be leveraged as OOD proxies. By fine-tuning these models on specific datasets and comparing likelihood ratios, OOD instances can be identified. Model Fusion: Combining information from different modalities, such as text and images in multimodal models, can enhance the OOD detection capability. By calculating likelihood ratios across modalities, a more comprehensive understanding of OOD samples can be achieved. Ensemble Methods: Utilizing ensemble methods by combining multiple likelihood ratios from different models can improve the robustness of OOD detection across various domains. Ensemble techniques can help mitigate the limitations of individual models and enhance overall performance.

Q: What are the potential limitations or drawbacks of relying solely on the likelihood ratio as the OOD detection criterion, and how could it be combined with other techniques to further improve performance?

While the likelihood ratio-based OOD detection method is effective, it has some limitations that can be addressed by combining it with other techniques: Sensitivity to Model Biases: Likelihood ratios may be sensitive to biases present in the pretrained model, leading to inaccurate OOD detection. To mitigate this, calibration techniques can be employed to adjust the likelihood scores and improve the overall reliability of the method. Limited Discriminative Power: Likelihood ratios alone may not capture complex relationships in the data, especially in high-dimensional spaces like images. By incorporating feature-based methods or anomaly detection algorithms, the detection capability can be enhanced. Domain Shift: In cases of significant domain shifts, likelihood ratios may not be sufficient for accurate OOD detection. By integrating domain adaptation techniques or domain-specific priors, the method can better adapt to diverse data distributions. Complementary Techniques: Combining the likelihood ratio approach with uncertainty estimation methods like Bayesian neural networks or ensemble learning can provide a more comprehensive understanding of model uncertainty and improve OOD detection performance. Threshold Selection: Setting an appropriate threshold for the likelihood ratio is crucial. By incorporating threshold optimization algorithms or dynamic thresholding strategies based on model confidence, the method can achieve better balance between false positives and false negatives.

Q: Given the importance of OOD detection for the safety and reliability of AI systems, how might this work inspire the development of new OOD detection approaches that leverage the wealth of knowledge captured in large foundation models?

This work can inspire the development of new OOD detection approaches by showcasing the effectiveness of leveraging the knowledge embedded in large foundation models. Here are some ways this work might inspire new approaches: Incorporating Meta-Learning: By utilizing meta-learning techniques, models can adapt quickly to new domains and tasks, enhancing their ability to detect OOD samples. Meta-learning can leverage the knowledge encoded in large foundation models to improve generalization to unseen data. Active Learning Strategies: Incorporating active learning methods can help the model actively query OOD samples for training, leveraging the wealth of knowledge in large models to iteratively improve OOD detection performance. Self-Supervised Learning: By integrating self-supervised learning techniques, models can learn representations that capture underlying data structures, enabling more effective OOD detection. Leveraging the knowledge captured in large foundation models can enhance the quality of self-supervised representations. Adversarial Training: Adversarial training can be employed to enhance the robustness of OOD detection models against adversarial attacks. By leveraging the knowledge from large foundation models, adversarial training can be more effective in detecting OOD samples. Interpretable Models: Developing interpretable models that can explain the OOD detection decisions based on the knowledge learned from large foundation models can enhance trust and transparency in AI systems. Leveraging the knowledge captured in these models can facilitate the development of more interpretable OOD detection approaches.

核心概念

Pretrained large language models can effectively serve as OOD proxies, and the likelihood ratio between a pretrained LLM and its finetuned variant provides a powerful criterion for detecting out-of-distribution data.

摘要

The paper revisits the use of the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection. The key insights are:

Pretrained LLMs can function as effective OOD proxies, as they encompass broad knowledge about language and can distinguish in-distribution data from OOD data.
Calculating the likelihood ratio between the pretrained LLM and its finetuned counterpart serves as a robust OOD detection criterion, leveraging the prior knowledge in the base model and the specialized knowledge in the finetuned model.
This approach is particularly convenient as practitioners often already have access to both the pretrained and finetuned LLMs, eliminating the need for additional training.
The authors evaluate the method across various scenarios, including far OOD, near OOD, spam detection, and question-answering (QA) systems. The results demonstrate the effectiveness of the likelihood ratio in identifying OOD instances.
For QA systems, the authors propose a novel approach that generates an answer using the finetuned LLM and then applies the likelihood ratio criterion to the question-answer pair, leading to improved OOD question detection.

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統計資料

The ducks in Janet's farm lay 16 eggs per day.
Janet eats 3 eggs for breakfast every morning.
Janet bakes muffins for her friends every day using 4 eggs.
Janet sells the remaining fresh duck eggs at the farmers' market for $2 per egg.

引述

"Guided by this insight, we discover that the likelihood ratio between the base model and its finetuned counterpart serves as an effective criterion for detecting OOD data."
"Leveraging the power of LLMs, we show that, for the first time, the likelihood ratio can serve as an effective OOD detector."

從以下內容提煉的關鍵洞見

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector

by Andi Zhang,T... 於 arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08679.pdf

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector

深入探究

How can the proposed likelihood ratio-based OOD detection method be extended to other domains beyond language models, such as computer vision or audio processing?

The likelihood ratio-based OOD detection method proposed in the context of language models can be extended to other domains like computer vision or audio processing by adapting the fundamental principles to suit the specific characteristics of these domains. Here are some ways to extend the method:

Feature Extraction: In computer vision, features extracted from images or videos can be used to calculate likelihood ratios between a pretrained model and its finetuned version. The likelihood of the features extracted from the data can serve as a proxy for in-distribution and out-of-distribution samples.

Spectrogram Analysis: For audio processing, spectrogram features can be used to compute likelihood ratios. By comparing the likelihood of spectrogram patterns between a base model and a finetuned model, OOD audio samples can be detected effectively.

Transfer Learning: Similar to language models, pretrained models in computer vision or audio processing can be leveraged as OOD proxies. By fine-tuning these models on specific datasets and comparing likelihood ratios, OOD instances can be identified.

Model Fusion: Combining information from different modalities, such as text and images in multimodal models, can enhance the OOD detection capability. By calculating likelihood ratios across modalities, a more comprehensive understanding of OOD samples can be achieved.

Ensemble Methods: Utilizing ensemble methods by combining multiple likelihood ratios from different models can improve the robustness of OOD detection across various domains. Ensemble techniques can help mitigate the limitations of individual models and enhance overall performance.

What are the potential limitations or drawbacks of relying solely on the likelihood ratio as the OOD detection criterion, and how could it be combined with other techniques to further improve performance?

While the likelihood ratio-based OOD detection method is effective, it has some limitations that can be addressed by combining it with other techniques:

Sensitivity to Model Biases: Likelihood ratios may be sensitive to biases present in the pretrained model, leading to inaccurate OOD detection. To mitigate this, calibration techniques can be employed to adjust the likelihood scores and improve the overall reliability of the method.

Limited Discriminative Power: Likelihood ratios alone may not capture complex relationships in the data, especially in high-dimensional spaces like images. By incorporating feature-based methods or anomaly detection algorithms, the detection capability can be enhanced.

Domain Shift: In cases of significant domain shifts, likelihood ratios may not be sufficient for accurate OOD detection. By integrating domain adaptation techniques or domain-specific priors, the method can better adapt to diverse data distributions.

Complementary Techniques: Combining the likelihood ratio approach with uncertainty estimation methods like Bayesian neural networks or ensemble learning can provide a more comprehensive understanding of model uncertainty and improve OOD detection performance.

Threshold Selection: Setting an appropriate threshold for the likelihood ratio is crucial. By incorporating threshold optimization algorithms or dynamic thresholding strategies based on model confidence, the method can achieve better balance between false positives and false negatives.

Given the importance of OOD detection for the safety and reliability of AI systems, how might this work inspire the development of new OOD detection approaches that leverage the wealth of knowledge captured in large foundation models?

This work can inspire the development of new OOD detection approaches by showcasing the effectiveness of leveraging the knowledge embedded in large foundation models. Here are some ways this work might inspire new approaches:

Incorporating Meta-Learning: By utilizing meta-learning techniques, models can adapt quickly to new domains and tasks, enhancing their ability to detect OOD samples. Meta-learning can leverage the knowledge encoded in large foundation models to improve generalization to unseen data.

Active Learning Strategies: Incorporating active learning methods can help the model actively query OOD samples for training, leveraging the wealth of knowledge in large models to iteratively improve OOD detection performance.

Self-Supervised Learning: By integrating self-supervised learning techniques, models can learn representations that capture underlying data structures, enabling more effective OOD detection. Leveraging the knowledge captured in large foundation models can enhance the quality of self-supervised representations.

Adversarial Training: Adversarial training can be employed to enhance the robustness of OOD detection models against adversarial attacks. By leveraging the knowledge from large foundation models, adversarial training can be more effective in detecting OOD samples.

Interpretable Models: Developing interpretable models that can explain the OOD detection decisions based on the knowledge learned from large foundation models can enhance trust and transparency in AI systems. Leveraging the knowledge captured in these models can facilitate the development of more interpretable OOD detection approaches.