Evaluating the Uncertainty Estimation Capabilities of Large Language and Vision-Language Models

Alapfogalmak

Large language models (LLMs) and vision-language models (VLMs) exhibit poor capability for accurately estimating their own uncertainty, often exhibiting overconfidence in their outputs across various natural language processing and image recognition tasks.

Kivonat

This study evaluates the uncertainty estimation capabilities of four state-of-the-art LLMs (GPT-4, GPT-3.5, LLaMA-2-70b, and PaLM 2) and two VLMs (GPT-4V and Gemini Pro Vision) across different tasks.
For the LLMs, the tasks include sentiment analysis (both binary and float labels), math word problems, and named-entity recognition. The results show that the LLMs generally exhibit poor calibration, with a tendency towards overconfidence, except for the sentiment analysis task where some models showed underconfidence. GPT-4 demonstrated the best calibration among the LLMs tested.
For the VLMs, a new dataset called Japanese Uncertain Scenes (JUS) was created to test their uncertainty estimation capabilities in an image recognition task. The results indicate that both GPT-4V and Gemini Pro Vision struggle with accurately estimating their uncertainty, with a predominant trend towards overconfidence. GPT-4V showed relatively better calibration compared to Gemini Pro Vision.
The study introduces a new metric called Net Calibration Error (NCE) to assess the direction of miscalibration (overconfidence or underconfidence) in addition to the commonly used Expected Calibration Error (ECE) and Maximum Calibration Error (MCE). The findings highlight the need for further research to improve uncertainty estimation in LLMs and VLMs, as accurate uncertainty quantification is crucial for the reliable deployment of these models.

Statisztikák

"GPT-4 has a mean accuracy of 92.0% and a mean confidence of 78.5% on the sentiment analysis binary task, with an ECE of 13.5% and an NCE of 13.5%, indicating underconfidence."
"GPT-3.5 has a mean accuracy of 25.0% and a mean confidence of 99.8% on the math word problems task, with an ECE of 74.8% and an NCE of -74.8%, indicating severe overconfidence."
"GPT-4V has a mean accuracy of 51.2% and a mean confidence of 62.6% on the image recognition task on the JUS dataset, with an ECE of 11.3% and an NCE of -11.3%, indicating overconfidence."

Idézetek

"This study aims to expand the domain of uncertainty estimation in LLMs by comparing four state-of-the-art LLMs: GPT-4, GPT-3.5, LLaMA-2-70b, and PaLM 2, across three distinct NLP tasks: sentiment analysis, math word problems, and named-entity recognition."
"Additionally, the quality of uncertainty estimation in VLMs is evaluated by testing two newly introduced VLMs, GPT-4V and Gemini Pro Vision, on a novel image recognition task."
"The results show that both LLMs and VLMs have a high calibration error and are overconfident most of the time, indicating a poor capability for uncertainty estimation."

Főbb Kivonatok

Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models

by Tobias Groot... : arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02917.pdf

Mélyebb kérdések

How can the uncertainty estimation capabilities of LLMs and VLMs be improved through architectural modifications or training approaches?

To enhance the uncertainty estimation capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs), several architectural modifications and training approaches can be considered:

Bayesian Neural Networks: Implementing Bayesian neural networks can enable models to provide probabilistic outputs, allowing for better uncertainty estimation. By incorporating Bayesian methods into the architecture, models can capture uncertainty in their predictions more effectively.

Ensemble Methods: Training multiple models and combining their predictions through ensemble methods can improve uncertainty estimation. Ensemble methods can provide a more robust estimation by leveraging the diversity of multiple models.

Calibration Layers: Introducing calibration layers in the model architecture can help align confidence scores with prediction accuracy. These layers can adjust the model's output to better reflect the true uncertainty in its predictions.

Task-Specific Training: Tailoring the training process to focus on uncertainty estimation tasks can improve the model's ability to express uncertainty. By including specific loss functions or regularization techniques during training, models can learn to provide more accurate uncertainty estimates.

Meta-Learning: Utilizing meta-learning techniques can enable models to adapt to new tasks and improve their uncertainty estimation capabilities. Meta-learning can help models generalize better and make more informed uncertainty estimates.

Active Learning: Incorporating active learning strategies during training can help models identify areas of uncertainty and prioritize learning from those instances. By actively seeking out challenging examples, models can improve their uncertainty estimation over time.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay can prevent models from being overly confident in their predictions. Regularization helps prevent overfitting and encourages models to express more realistic uncertainty levels.

By implementing these architectural modifications and training approaches, LLMs and VLMs can enhance their uncertainty estimation capabilities and provide more reliable and trustworthy predictions in various tasks.

What are the potential implications of overconfident outputs from LLMs and VLMs in real-world applications, and how can these risks be mitigated?

Overconfident outputs from Large Language Models (LLMs) and Vision-Language Models (VLMs) can have significant implications in real-world applications, including:

Misinformation: Overconfident models may provide incorrect or misleading information, leading to the spread of misinformation in applications such as content generation, chatbots, and image recognition.

Decision-Making: In critical decision-making processes, overconfident models can provide false assurances, leading to poor decisions based on inaccurate predictions.

Safety Concerns: In applications like autonomous vehicles or medical diagnosis, overconfident models can pose safety risks if they provide incorrect recommendations or predictions.

Trust Issues: Overconfident models may erode trust in AI systems if users consistently receive inaccurate or overconfident responses, leading to a lack of confidence in the technology.

To mitigate the risks associated with overconfident outputs from LLMs and VLMs, the following strategies can be implemented:

Calibration Techniques: Implement calibration techniques to align model confidence scores with prediction accuracy. Calibration layers or post-processing methods can adjust confidence levels to reflect the true uncertainty in predictions.

Uncertainty Quantification: Incorporate uncertainty quantification methods to provide a measure of confidence in model predictions. By explicitly expressing uncertainty levels, users can make more informed decisions based on the model's outputs.

Human-in-the-Loop: Introduce human oversight or intervention in critical applications to verify model predictions and correct errors. Human-in-the-loop systems can help mitigate the impact of overconfident outputs by providing human judgment and validation.

Regular Evaluation: Regularly evaluate model performance and calibration to identify instances of overconfidence. Continuous monitoring and feedback loops can help detect and address overconfident outputs in real-time.

Transparency and Explainability: Enhance model transparency and explainability to help users understand the basis of model predictions. Providing explanations for model decisions can increase trust and enable users to assess the reliability of the outputs.

By implementing these mitigation strategies, the risks associated with overconfident outputs from LLMs and VLMs can be minimized, ensuring more reliable and trustworthy AI applications in various domains.

How might the findings of this study on uncertainty estimation relate to the broader challenge of ensuring the reliability and robustness of large-scale AI systems?

The findings of this study on uncertainty estimation in Large Language Models (LLMs) and Vision-Language Models (VLMs) are crucial in addressing the broader challenge of ensuring the reliability and robustness of large-scale AI systems.

Model Trustworthiness: Uncertainty estimation is essential for assessing the trustworthiness of AI systems. Models that can accurately express uncertainty provide users with valuable insights into the reliability of their predictions, enhancing trust in the technology.

Risk Management: Understanding and quantifying uncertainty in AI predictions is key to effective risk management. By identifying areas of uncertainty, stakeholders can make informed decisions and mitigate potential risks associated with model inaccuracies.

Decision-Making: Reliable uncertainty estimation enables better decision-making in various applications. Decision-makers can factor in uncertainty levels when using AI systems to make critical choices, leading to more informed and reliable outcomes.

Ethical Considerations: Uncertainty estimation plays a role in addressing ethical considerations in AI systems. By providing transparency around uncertainty levels, models can adhere to ethical principles such as fairness, accountability, and transparency.

Continual Improvement: By evaluating and improving uncertainty estimation capabilities, AI systems can evolve to be more reliable and robust over time. Continuous monitoring and enhancement of uncertainty estimation contribute to the overall improvement of AI systems.

Generalization and Adaptability: Models with accurate uncertainty estimation are better equipped to generalize to new tasks and adapt to changing environments. This adaptability enhances the reliability and robustness of AI systems in diverse scenarios.

In conclusion, the findings of this study underscore the importance of uncertainty estimation in ensuring the reliability and robustness of large-scale AI systems. By addressing uncertainty challenges, AI systems can become more trustworthy, effective, and ethical in their applications, contributing to the advancement of responsible AI development.