Rethinking Uncertainty Estimation in Natural Language Generation (2412.15176v1)

Abstract: LLMs are increasingly employed in real-world applications, driving the need to evaluate the trustworthiness of their generated text. To this end, reliable uncertainty estimation is essential. Since current LLMs generate text autoregressively through a stochastic process, the same prompt can lead to varying outputs. Consequently, leading uncertainty estimation methods generate and analyze multiple output sequences to determine the LLM's uncertainty. However, generating output sequences is computationally expensive, making these methods impractical at scale. In this work, we inspect the theoretical foundations of the leading methods and explore new directions to enhance their computational efficiency. Building on the framework of proper scoring rules, we find that the negative log-likelihood of the most likely output sequence constitutes a theoretically grounded uncertainty measure. To approximate this alternative measure, we propose G-NLL, which has the advantage of being obtained using only a single output sequence generated by greedy decoding. This makes uncertainty estimation more efficient and straightforward, while preserving theoretical rigor. Empirical results demonstrate that G-NLL achieves state-of-the-art performance across various LLMs and tasks. Our work lays the foundation for efficient and reliable uncertainty estimation in natural language generation, challenging the necessity of more computationally involved methods currently leading the field.

• The paper introduces a novel uncertainty estimation method using the negative log-likelihood of the most likely sequence.
• It replaces computationally expensive multiple sampling with a single greedy-decoded sequence while maintaining accuracy.
• Empirical evaluations show the approach matches or exceeds traditional methods across diverse model architectures.

Rethinking Uncertainty Estimation in Natural Language Generation

The paper "Rethinking Uncertainty Estimation in Natural Language Generation" presents a comprehensive paper of uncertainty estimation techniques in the context of autoregressive LLMs. It focuses on making uncertainty estimation more computationally effective without compromising on accuracy, which is crucial for assessing the reliability of generated text.

Key Contributions

1. Critique of Existing Methods: The paper begins by analyzing existing uncertainty estimation methods that rely on generating multiple output sequences to infer the level of uncertainty. Such methods are computationally expensive, limiting their practical applicability at scale.
2. Alternative Proposition: To address these limitations, the authors propose leveraging the negative log-likelihood of the most likely output sequence as a measure of uncertainty, rather than inspecting multiple outputs. This measure is grounded in the principles of proper scoring rules and is argued to be a theoretically sound approach.
3. Methodological Innovation: By using a singular output sequence generated via greedy decoding, which involves choosing the most likely token at each step, the proposed method significantly reduces the computational requirements associated with uncertainty estimation, while maintaining or even exceeding current state-of-the-art performance.

Empirical Evaluations

• Performance Metrics: Throughout various scenarios, the proposed approach matches or outperforms existing methods in terms of accuracy and reliability across several model architectures and sizes. This demonstrates the robustness and effectiveness of utilizing the most probable sequence for estimating uncertainty.
• Efficiency Gains: Unlike traditional methods requiring multiple sample generations, the proposed technique's reliance on a single sequence drastically reduces computational costs and complexity, making it significantly more viable for real-world deployments.

Implications and Future Directions

• Practical Implications: In practical terms, this method democratizes the use of uncertainty estimation by making it accessible to applications constrained by computational costs. This can enhance the implementation of LLMs in scenarios where reliability is critical, such as in medical or legal advice systems.
• Theoretical Impacts: The introduction of a theoretically justified, computationally efficient uncertainty metric raises important questions about the trade-offs between computational expense and estimation accuracy in LLMs’ deployment.
• Future Research: The paper opens several pathways for future research, including the potential integration of semantic understanding into the proposed measure to further enhance its efficacy. Additionally, adapting the method to varying context lengths and task requirements remains an open area for exploration.

Overall, this paper offers a significant contribution to the body of work on uncertainty in natural language generation by proposing a method that balances the need for accuracy and practicality. It sets the stage for more operationalized uncertainty estimation techniques, which can serve as foundational components for the development of more trustworthy and accountable AI systems.