Adaptive Conformal Prediction for Improving Factuality of Generations by Large Language Models
Summary: arXiv:2604.13991v1 Announce Type: cross
Abstract
Large language models (LLMs) have transformed natural language processing; however, they are often prone to generating factually incorrect outputs. Recent advancements have applied conformal prediction techniques to provide uncertainty estimates and statistical guarantees regarding the factuality of LLM outputs. Despite these advancements, current methodologies are typically not prompt-adaptive, which restricts their ability to capture the variability that depends on specific inputs. This limitation can lead to two significant issues: over-coverage, where too few items are filtered out, and under-coverage, where too many items are excluded based on a given prompt or task.
Introduction
In light of these challenges, we propose a novel adaptive conformal prediction approach that enhances existing conformal score transformation methods tailored for LLMs. This approach shows promise for applications in long-form generation and multiple-choice question answering. By enabling prompt-dependent calibration, our method retains marginal coverage guarantees while achieving significant improvements in conditional coverage.
Key Features of the Proposed Approach
- Prompt-Dependent Calibration: Our method adjusts its predictions based on the specific context provided by the prompt, allowing for more accurate and relevant outputs.
- Marginal and Conditional Coverage: The approach maintains essential statistical guarantees while improving the reliability of generated responses.
- Selective Prediction: It allows for the filtering of unreliable claims or answer choices, enhancing the overall quality of downstream applications.
Evaluation and Results
We conducted extensive evaluations across several white-box models and diverse application domains to assess the efficacy of our adaptive conformal prediction approach. The results indicate that our method significantly outperforms existing baselines in terms of conditional coverage, addressing the limitations of previous approaches effectively.
Conclusion
As large language models continue to evolve, the need for reliable factuality in their outputs becomes increasingly critical. Our adaptive conformal prediction technique represents a significant advancement in this field, providing a robust framework for improving the factual accuracy of LLM generations. By leveraging prompt-adaptive techniques, we can better align model outputs with user expectations, ultimately leading to more trustworthy AI applications.
Future Directions
Looking ahead, further research will focus on refining our approach and exploring its applicability in other areas of natural language processing. We aim to enhance the adaptability of our methods and investigate their potential for real-time applications where factual accuracy is paramount.