Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
Quick Answer
This paper introduces self-function vectors to quantify aleatoric uncertainty in In-Context Learning (ICL) for LLMs, enhancing prediction reliability.
Quick Take
The proposed evaluation protocol allows for precise separation of aleatoric from epistemic uncertainty, demonstrating improved measurement of predictions over existing methods. This advancement is crucial for applications like hallucination detection.
Key Points
- Introduces self-function vectors for modeling aleatoric uncertainty in LLMs.
- Proposes a new evaluation protocol for separating aleatoric and epistemic uncertainty.
- Demonstrates improved reliability in measuring LLM predictions under ICL.
- Framework applicable to real-world datasets and synthetic tasks.
- Supports trustworthy applications like hallucination detection.
Paper Resources
Source Excerpt
In-Context Learning (ICL) allows to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations. Uncertainty decomposition-separating aleatoric from epistemic sources-is particularly crucial in this setting, yet existing methods, designed for standard generation tasks, fail to capture the
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