MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs (2505.24858v1)

Abstract: A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-reliance and eroded trust. We present the first systematic study of $\textit{faithful confidence calibration}$ of LLMs, benchmarking models' ability to use linguistic expressions of uncertainty that $\textit{faithfully reflect}$ their intrinsic uncertainty, across a comprehensive array of models, datasets, and prompting strategies. Our results demonstrate that LLMs largely fail at this task, and that existing interventions are insufficient: standard prompt approaches provide only marginal gains, and existing, factuality-based calibration techniques can even harm faithful calibration. To address this critical gap, we introduce MetaFaith, a novel prompt-based calibration approach inspired by human metacognition. We show that MetaFaith robustly improves faithful calibration across diverse models and task domains, enabling up to 61% improvement in faithfulness and achieving an 83% win rate over original generations as judged by humans.

Faithful Natural Language Uncertainty Expression in LLMs: A Study on MetaFaith

"MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs" explores the challenge of aligning intrinsic uncertainty in LLMs with their linguistic expressions of uncertainty. This paper addresses a latent issue in LLMs concerning the misalignment between a model's internal confidence and its outward expressions, which can lead to users' misplaced trust in AI systems. Through a systematic examination across numerous models, datasets, and prompting strategies, the paper reveals the deficiencies in current LLMs' capacity to faithfully express uncertainty.

Main Findings and Contributions

1. Benchmarking Faithful Calibration: The paper presents the first wide-range systematic benchmarking of LLMs' ability to calibrate linguistic expressions of uncertainty with intrinsic uncertainty. Despite advancements in LLM technology, the research underscores existing models' failures in aligning these uncertainties, highlighting a critical gap in the deployment of AI systems.
2. Inadequacy of Current Methods: The authors analyze existing interventions aimed at faithful calibration and find them largely ineffective. Standard prompting approaches only marginally enhance faithfulness, and factuality-based calibration techniques can even impair it, suggesting that fact-based confidence alignment does not necessarily translate to effective uncertainty communication.
3. Introduction of MetaFaith: Inspired by principles of human metacognition, the paper introduces MetaFaith, a novel approach for improving faithful calibration in LLMs. This method leverages metacognitive prompting, encouraging LLMs to reflect on their intrinsic confidence and communicate it more accurately in natural language. MetaFaith is shown to improve faithfulness by up to 61% over a range of models and domains. Importantly, it is a task-agnostic solution that does not require model fine-tuning or access to internal model weights, making it a cost-effective tool for enhancing LLM reliability.
4. Empirical Evidence of Success: Extensive experiments validate the efficacy of MetaFaith. When subjected to various datasets and LLM architectures, MetaFaith systematically enhances the alignment between intrinsic and expressed uncertainty. Remarkably, human annotations verify MetaFaith's ability to produce more trustworthy and reliable AI outputs, achieving an 83% win rate over baseline uncertainty prompts.
5. Divergence from Factual Calibration: The paper elucidates a critical divergence between faithful and factual calibration. While factual calibration aligns model confidence with accuracy, it disregards the end-to-end impact of linguistic assertiveness on perceived model reliability. This research highlights the necessity of addressing both dimensions to bolster user trust and improve the practical applicability of LLMs.

Implications and Future Directions

The research into MetaFaith opens several avenues for future exploration and development within natural language processing and AI. By laying the groundwork for more faithful uncertainty expression, this paper paves the way for improved interaction between humans and AI. It stresses the need for LLMs that can transparently convey their limitations, reducing over-reliance on AI and enhancing user discernment in decision-making processes.

Moreover, the work calls attention to the ethical and design considerations required for deploying LLMs in high-stakes environments where reliability is paramount. Future research could explore refining metacognitive strategies and exploring their integration with other calibration methodologies to further augment the credibility of AI systems. Additionally, expanding the scope of this research to address cross-linguistic and cultural variations in uncertainty expression could enhance the global applicability of these findings.

In conclusion, "MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs" is a pivotal paper that addresses an often overlooked aspect of AI trustworthiness. By promoting faithful calibration through metacognitive principles, it not only enhances the reliability of LLMs but also contributes to the broader discourse on ethical AI deployment. This research is poised to significantly influence future developments in AI interpretability and user interaction frameworks.