Mitigating Hallucinations in Large Language Models via Conformal Abstention

Core Concepts

Large language models can confidently generate responses that are incorrect or nonsensical (hallucinations). This work proposes a principled procedure to determine when the model should abstain from responding, instead of hallucinating, by leveraging the model's self-consistency as a measure of confidence.

Abstract

The authors develop a method to mitigate hallucinations in large language models (LLMs) by determining when the model should abstain from responding. The key ideas are: Use the LLM itself to self-evaluate the similarity between its sampled responses for a given query. This provides a measure of the model's confidence in its responses. Leverage conformal prediction techniques to develop an abstention procedure that benefits from rigorous theoretical guarantees on the hallucination rate (error rate). The method works as follows: Generate multiple responses from the LLM for a given query. Compute a score based on the similarity between the responses, either by counting the number of similar responses (match count) or estimating the expected number of similar responses (expected match count). Use conformal prediction to determine a threshold on the score, below which the model should abstain from responding. This approach is evaluated on closed-book, open-domain question answering datasets. It is shown to reliably bound the hallucination rate while maintaining a significantly less conservative abstention rate compared to baselines using log-probability scores to quantify uncertainty. The authors also provide a method for calibrating the threshold used to determine if two responses match, based on conformal prediction, with theoretical guarantees on the accuracy of the match prediction.

Stats

The abstention rate is the expected proportion of time the method chooses to abstain from responding. The hallucination risk is the expected proportion of unfiltered hallucinations in the responses.

Quotes

"Large language models (LLMs) are excellent at next word prediction. At the same time, however, they are also prone to hallucination—that is, confidently generate responses that may look plausible on the surface, but that are actually incorrect or even nonsensical." "Hallucinations can be extremely detrimental towards achieving trustworthy and reliable LLM performance, and hence avoiding or even detecting hallucinations has become one of the most important research topics in LLM research."

Key Insights Distilled From

Mitigating LLM Hallucinations via Conformal Abstention

by Yasi... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01563.pdf

Deeper Inquiries

How could the proposed method be extended to handle open-ended generation tasks beyond question answering

The proposed method for mitigating hallucinations in LLMs via conformal abstention could be extended to handle open-ended generation tasks beyond question answering by adapting the scoring and matching functions to suit the specific requirements of the task. For instance, in text generation tasks, the similarity function could be tailored to measure coherence and relevance of generated text segments. Additionally, the threshold for determining matches could be adjusted based on the nature of the generated content. By incorporating domain-specific metrics and criteria into the scoring and matching functions, the method can be customized to effectively detect hallucinations in a variety of open-ended generation tasks.

What are the limitations of using self-consistency as a proxy for detecting hallucinations, and how could these be addressed

Using self-consistency as a proxy for detecting hallucinations has limitations, primarily due to the inability to detect situations where the LLM is confident in providing an incorrect answer. To address these limitations, one approach could be to incorporate external validation mechanisms, such as fact-checking databases or expert annotations, to verify the accuracy of the generated responses. Additionally, leveraging ensemble methods to compare responses generated by multiple models could enhance the reliability of the self-consistency measure. By combining self-consistency with external validation and ensemble techniques, the method can improve its ability to detect and mitigate hallucinations in LLMs more effectively.

What insights from this work on mitigating hallucinations in LLMs could be applied to improving the reliability and trustworthiness of other types of AI systems

Insights from this work on mitigating hallucinations in LLMs can be applied to improving the reliability and trustworthiness of other types of AI systems by implementing similar calibration and abstention mechanisms. For instance, in image recognition systems, confidence scores could be calibrated using conformal prediction to determine when to abstain from providing a classification. In speech recognition systems, self-consistency measures could be used to detect erroneous transcriptions and trigger an abstention mechanism. By integrating these techniques into various AI systems, it is possible to enhance their robustness and accuracy, ultimately improving user trust and confidence in the technology.