Learning Uncertainty from Sequential Internal Dispersion in Large Language Models
Summary: arXiv:2604.15741v1 Announce Type: cross
Abstract
Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate uncertainty. However, they suffer from strict assumptions on how hidden states should evolve across layers, and from information loss by solely focusing on last or mean tokens.
Introduction
As the deployment of large language models continues to grow, the need for effective uncertainty estimation becomes increasingly critical. These models have shown remarkable capabilities, yet they are not immune to generating incorrect or nonsensical outputs—a phenomenon often referred to as “hallucination.” Traditional methods of estimating uncertainty have their limitations, primarily due to their reliance on specific assumptions regarding the evolution of internal states across various layers of the model.
Challenges with Current Approaches
Current methodologies for uncertainty estimation often focus on simplifying the representation of internal states. This simplification typically leads to:
- Strict Assumptions: Many existing models impose rigid frameworks on how hidden states should change across different layers, limiting their adaptability.
- Information Loss: By concentrating solely on the last or average tokens, these models can discard valuable information present in earlier layers.
Introducing Sequential Internal Variance Representation (SIVR)
To address the limitations of current approaches, we propose a novel framework called Sequential Internal Variance Representation (SIVR). This method leverages token-wise, layer-wise features derived from the hidden states of language models, providing a more nuanced approach to uncertainty estimation.
Key Features of SIVR
- Model Agnostic: SIVR operates on the fundamental principle that uncertainty can be observed through the degree of dispersion or variance in internal representations across layers. This flexibility allows it to be applicable across different models and tasks.
- Temporal Pattern Learning: By aggregating the full sequence of per-token variance features, SIVR learns temporal patterns indicative of factual errors, thus preventing the loss of crucial information.
- Generalization: Experimental results indicate that SIVR consistently outperforms strong baseline models in various tasks, demonstrating its potential for robust performance.
Experimental Results
The experiments conducted reveal that SIVR not only achieves superior performance compared to existing methods but also exhibits stronger generalization capabilities. This is particularly noteworthy as it does not require extensive training datasets, making it more suitable for real-world applications.
Conclusion
The introduction of Sequential Internal Variance Representation marks a significant advancement in the field of uncertainty estimation within large language models. By addressing the shortcomings of prior methods, SIVR paves the way for more reliable and effective detection of hallucinations. Researchers and practitioners interested in implementing this framework can access the code repository at https://github.com/ponhvoan/internal-variance.