Identifying Unreliable Responses from Black-Box Vision-Language Models Using Neighborhood Consistency

Core Concepts

Consistency over rephrasings of a visual question can be used to identify unreliable predictions from a black-box vision-language model, even when the rephrasing model is substantially smaller than the black-box model.

Abstract

The paper explores the problem of selective visual question answering using black-box vision-language models, where the model is allowed to abstain from answering a question if it is not confident in the prediction.
The key insights are:

Existing approaches to selective prediction typically require access to the internal representations of the model or retraining the model, which is not feasible in a black-box setting.

The authors propose using the principle of neighborhood consistency to identify unreliable responses from a black-box vision-language model. The intuition is that a reliable response should be consistent across semantically equivalent rephrasings of the original question.

Since it is not possible to directly sample neighbors in feature space in a black-box setting, the authors use a smaller proxy model to approximately sample rephrasings of the original question.

The authors find that the consistency of the black-box model's responses over the rephrasings can be used to identify model responses that are likely to be unreliable, even in adversarial settings or settings that are out-of-distribution to the proxy model.

Experiments on in-distribution, out-of-distribution, and adversarial visual questions show that consistency over rephrasings is correlated with model accuracy, and predictions that are highly consistent over rephrasings are more likely to be correct.

The approach works even when the rephrasing model is substantially smaller than the black-box model, making it a practical solution for using large, black-box vision-language models in safety-critical applications.

Stats

"Consistency over rephrasings of a visual question can be used to identify unreliable predictions from a black-box vision-language model."
"The approach works even when the rephrasing model is substantially smaller than the black-box model."

Quotes

"Consistency over rephrasings of a visual question can be used to identify unreliable predictions from a black-box vision-language model, even when the rephrasing model is substantially smaller than the black-box model."
"Consistency over the rephrasings of a question is correlated with model accuracy on the original question, and predictions that are highly consistent over rephrasings are more likely to be correct."

Key Insights Distilled From

Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering

by Zaid Khan,Yu... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10193.pdf

Deeper Inquiries

How can the proposed approach be extended to other multimodal tasks beyond visual question answering?

The proposed approach of using consistency over rephrasings to assess the reliability of a black-box model can be extended to various other multimodal tasks beyond visual question answering. One way to extend this approach is by applying it to tasks like image captioning, visual dialog, and image-text retrieval. In image captioning, the model can generate multiple captions for an image and assess the consistency of the generated captions. Similarly, in visual dialog, the model can provide responses to dialog questions and evaluate the consistency of its answers. For image-text retrieval, the model can retrieve relevant text descriptions for images and analyze the consistency of the retrieved texts.
By adapting the concept of consistency over rephrasings to these multimodal tasks, we can effectively evaluate the reliability of the model's outputs across different modalities. This extension can help in improving the trustworthiness and accuracy of black-box models in various multimodal applications.

What are the limitations of using a proxy model to generate rephrasings, and how can the quality of the rephrasings be improved?

Using a proxy model to generate rephrasings for assessing the reliability of a black-box model has certain limitations. One limitation is the potential discrepancy in the quality of rephrasings generated by the proxy model compared to the original model. The proxy model may not capture all the nuances and intricacies of the original model, leading to variations in the quality of rephrasings.
To improve the quality of rephrasings generated by the proxy model, several strategies can be implemented. Firstly, fine-tuning the proxy model on a diverse and representative dataset can enhance its ability to generate accurate rephrasings. Additionally, incorporating techniques like data augmentation, transfer learning, and adversarial training can help in improving the robustness and quality of the rephrasings.
Furthermore, leveraging advanced natural language processing models with enhanced capabilities in text generation, such as transformer-based models like GPT-3 or BERT, can also elevate the quality of rephrasings. By utilizing state-of-the-art language models, the proxy model can produce more contextually relevant and coherent rephrasings, thereby enhancing the effectiveness of the proposed approach.

How can the insights from this work be applied to improve the robustness and reliability of large language models in general?

The insights from this work can be instrumental in enhancing the robustness and reliability of large language models in various applications. One key application is in improving the interpretability and trustworthiness of black-box language models. By incorporating the concept of consistency over rephrasings, models can be evaluated for their reliability in generating consistent outputs across different linguistic variations.
Moreover, the approach of assessing predictive uncertainty through consistency can aid in identifying high-risk inputs and potential errors in model predictions. This can be particularly valuable in safety-critical scenarios where the accuracy and reliability of language models are paramount.
Additionally, the methodology of using a proxy model to generate rephrasings can be leveraged to enhance the training and validation processes of large language models. By employing proxy models for data augmentation, error analysis, and uncertainty estimation, the overall performance and robustness of language models can be improved.
Overall, by integrating the insights from this work into the development and evaluation of large language models, researchers and practitioners can advance the reliability and trustworthiness of these models in real-world applications.