arXiv:2606.02093v1 Announce Type: new Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ). However, while uncertainty metrics capture when models lack knowledge or capacity to make a prediction, they also reflect aleatoric uncertainty, which is inherent in the model input and context. This paper presents a method for improving error prediction for Large Language Models (LLMs), by disentangling input ambiguity from UQ signal. We conduct experiments on the task of Question Answering (QA) with six UQ
The proliferation of LLMs creates an urgent need for reliable uncertainty quantification, making advanced error prediction methods crucial for their safe and effective deployment.
Improved error prediction based on disentangling ambiguity will increase the trustworthiness and reliability of AI systems, particularly Large Language Models, across critical applications.
The ability to differentiate between inherent input ambiguity and model uncertainty will allow for more sophisticated and targeted interventions in AI system design and deployment.
- · AI developers
- · Enterprises deploying LLMs
- · AI safety researchers
- · Systems with high inherent input ambiguity
- · Users relying on black-box AI outputs
More robust and dependable AI applications, especially in high-stakes environments.
Reduced need for extensive human oversight in certain AI-driven workflows as confidence in AI outputs grows.
Acceleration of autonomous AI agents by improving their ability to self-assess and rectify errors or escalate ambiguous situations.
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Read at arXiv cs.CL