Conformal Prediction with Large Language Models for Multi-Choice Question Answering

This research focuses on the importance of robust uncertainty quantification techniques for the safe deployment of large language models in high-stakes scenarios. Specifically, the study explores how conformal prediction can be used to provide uncertainty estimates in language models for multiple-choice question-answering tasks. The researchers find that the uncertainty estimates obtained from conformal prediction are closely correlated with prediction accuracy. This finding has significant implications for downstream applications, such as selective classification and filtering out low-quality predictions. Additionally, the study investigates the exchangeability assumption required by conformal prediction for out-of-subject questions, which is a more realistic scenario in many practical applications. By addressing these challenges, this work contributes to ensuring more trustworthy and reliable usage of large language models in safety-critical situations where robust guarantees of error rate are essential. In terms of methodology, experiments were conducted to evaluate the effectiveness of conformal prediction in providing uncertainty quantification for multiple-choice question-answering tasks. The results showed a strong correlation between uncertainty estimates and prediction accuracy, indicating that conformal prediction can effectively capture uncertainties in language models' responses. Furthermore, potential applications of these uncertainty estimates were explored. Selective classification and filtering out low-quality predictions were identified as valuable downstream applications that can benefit from leveraging the tight correlation between uncertainty estimates and prediction accuracy. The researchers also addressed an important aspect related to out-of-subject questions by investigating the exchangeability assumption required by conformal prediction when dealing with questions outside the model's training domain. This scenario is more representative of real-world applications where language models may encounter unfamiliar topics or subjects. By examining this assumption, the study provided insights into how conformal prediction performs when faced with out-of-subject questions. Overall, this research contributes to enhancing the trustworthiness and reliability of large language models in safety-critical situations by developing robust uncertainty quantification techniques through conformal prediction. The findings highlight the potential applications of these techniques in selective classification and filtering out low-quality predictions. Additionally, the investigation of the exchangeability assumption expands the understanding of how conformal prediction can handle out-of-subject questions, making it more applicable to practical scenarios.

Introduction

Large language models have become increasingly popular in recent years, with the development of advanced deep learning techniques and the availability of vast amounts of data. These models have shown impressive performance on various natural language processing tasks, such as text generation, translation, and question-answering. However, their deployment in high-stakes scenarios raises concerns about their reliability and trustworthiness. In particular, there is a need for robust uncertainty quantification techniques to ensure safe usage of large language models in critical applications.

This research paper focuses on addressing this need by exploring the use of conformal prediction for providing uncertainty estimates in language models for multiple-choice question-answering tasks. The study investigates the correlation between these uncertainty estimates and prediction accuracy and explores potential downstream applications that can benefit from leveraging this correlation. Additionally, the researchers address an important aspect related to out-of-subject questions by examining the exchangeability assumption required by conformal prediction.

Methodology

To evaluate the effectiveness of conformal prediction in providing uncertainty quantification for multiple-choice question-answering tasks, experiments were conducted using a pre-trained large language model called BERT (Bidirectional Encoder Representations from Transformers). This model was fine-tuned on two different datasets: RACE (ReAding Comprehension dataset from Examinations) and ARC (AI2 Reasoning Challenge). The researchers used three different metrics to measure performance: accuracy, calibration error, and expected calibration error.

The first set of experiments focused on evaluating how well conformal prediction captures uncertainties in BERT's responses. This was done by comparing the uncertainty estimates obtained from conformal prediction with BERT's actual prediction accuracy on both datasets. The results showed a strong correlation between these two measures, indicating that conformal prediction can effectively capture uncertainties in language models' responses.

Next, potential downstream applications were explored based on this tight correlation between uncertainty estimates and prediction accuracy. Selective classification, where only high-confidence predictions are accepted, and filtering out low-quality predictions were identified as valuable applications that can benefit from leveraging these uncertainty estimates.

Finally, the researchers investigated the exchangeability assumption required by conformal prediction when dealing with questions outside the model's training domain. This was done by creating a new dataset of out-of-subject questions and evaluating how well conformal prediction performs on them compared to in-domain questions. The results showed that while there is a slight decrease in performance for out-of-subject questions, conformal prediction still provides reliable uncertainty estimates.

Implications

The findings of this research have significant implications for the safe deployment of large language models in high-stakes scenarios. By using conformal prediction to provide uncertainty estimates, downstream applications such as selective classification and filtering out low-quality predictions can be improved. These techniques can help mitigate potential risks associated with relying on language models' responses without considering their uncertainties.

Moreover, the investigation of the exchangeability assumption expands our understanding of how conformal prediction performs when faced with out-of-subject questions. This is crucial for practical applications where language models may encounter unfamiliar topics or subjects.

Conclusion

In conclusion, this research highlights the importance of robust uncertainty quantification techniques for ensuring trustworthy and reliable usage of large language models in safety-critical situations. By using conformal prediction to provide uncertainty estimates, downstream applications such as selective classification and filtering out low-quality predictions can benefit from leveraging this tight correlation between uncertainties and prediction accuracy. Additionally, by addressing challenges related to out-of-subject questions through investigating the exchangeability assumption required by conformal prediction, this work contributes to expanding our understanding of how these techniques perform in real-world scenarios.