Non-Exchangeable Conformal Language Generation with Nearest Neighbors (2402.00707v1)

Abstract: Quantifying uncertainty in automatically generated text is important for letting humans check potential hallucinations and making systems more reliable. Conformal prediction is an attractive framework to provide predictions imbued with statistical guarantees, however, its application to text generation is challenging since any i.i.d. assumptions are not realistic. In this paper, we bridge this gap by leveraging recent results on non-exchangeable conformal prediction, which still ensures bounds on coverage. The result, non-exchangeable conformal nucleus sampling, is a novel extension of the conformal prediction framework to generation based on nearest neighbors. Our method can be used post-hoc for an arbitrary model without extra training and supplies token-level, calibrated prediction sets equipped with statistical guarantees. Experiments in machine translation and LLMing show encouraging results in generation quality. By also producing tighter prediction sets with good coverage, we thus give a more theoretically principled way to perform sampling with conformal guarantees.

Non-Exchangeable Conformal Language Generation with Nearest Neighbors

The paper under review, "Non-Exchangeable Conformal Language Generation with Nearest Neighbors" by Ulmer et al., presents an advanced methodology expanding the conformal prediction framework into the domain of natural language generation (NLG). The primary contribution of this work is the introduction of non-exchangeable conformal prediction techniques fortified by nearest neighbor retrieval, which provides a novel approach to generating statistically sound predictions in NLG tasks without requiring i.i.d. assumptions. This paper effectively integrates concepts of uncertainty quantification from machine learning into the challenging field of language generation, offering new insights into reliable text generation methods.

Framework and Methodology

The research describes a novel method called non-exchangeable conformal nucleus sampling, utilizing recent advances in non-exchangeable conformal prediction. The method is augmented by nearest neighbor search to improve the relevance of calibration data, thereby preserving statistical guarantees when traditional i.i.d. assumptions are violated. The algorithm dynamically curates a calibration set by retrieving relevant past instances using $k$-nearest neighbors, leveraging latent representations produced by LLMs like GPT-4 and OPT.

A significant feature of the approach is the ability to assess and constrain the generation of text by calibrating token-level prediction sets, which are endowed with statistical guarantees essential for maintaining quality and reliability. The proposed method leverages adaptive prediction sets that allow for flexible handling of sentences with varying complexities by incorporating the cumulative probability distributions of possible token continuations.

Experimental Validation

The effectiveness of the method is demonstrated through extensive experiments in domains such as machine translation (MT) and open text generation. Models including M2M100 and OPT are used to show the utility across different language processing tasks. The results demonstrate that the proposed method can achieve competitive or superior generation quality compared to standard sampling techniques while also offering the advantage of tighter prediction sets and better compliance with desired statistical coverage. The method's robustness is further evident when tested under distributional shifts, where it maintains intended coverage with adjustable degrees of prediction set sizes.

Results and Implications

The method provides an edge over conventional sampling techniques by producing smaller, well-calibrated prediction sets, thus efficiently capturing the model uncertainty. This capability not only benefits NLG activities by refining generated outputs but also supports applications requiring high reliability, such as machine translation and conversational interfaces.

The work's implications extend into future AI developments where it could inform systems that demand high assurance automation, ensuring that model predictions remain trustworthy, even under uncertain conditions. The introduction of nearest neighbor memory tunings in NLG represents a promising direction for enhancing the resilience of LLMs, with potential applications in adaptive text generation, robust interaction systems, and domains requiring precise uncertainty quantification.

Conclusion

The paper by Ulmer et al. skillfully integrates nearest-neighbor mechanisms in a non-exchangeable conformal prediction setting to address challenges posed by non-i.i.d. conditions in NLG. Through meticulous experimentation and concrete theoretical backing, the authors succeed in making a considerable contribution to making AI-generated text more reliable and consistent, establishing an important foundation for future work focused on deployment-ready LLMs. The paper opens up avenues for further exploration, such as incorporating diverse non-conformity scores or integrating the methodology into existing frameworks for more generalized applications.