CoP: Factual Inconsistency Detection by Controlling the Preference

TL;DR

This paper introduces CoP, an unsupervised framework that detects factual inconsistencies in summarization by controlling model preferences with prompts, achieving state-of-the-art results across multiple tasks.

Contribution

It proposes a novel unsupervised method using prompts to measure factual consistency preferences, enabling fine-grained inconsistency detection and extension to supervised learning.

Findings

Achieves new SOTA on three factual inconsistency detection tasks.

Effectively distinguishes specific inconsistency categories like entity and coreference.

Demonstrates the utility of prompt-controlled preference measurement.

Abstract

Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limits its practical applications. Previous work found that the probabilities assigned by the generation model reflect its preferences for the generated summary, including the preference for factual consistency, and the preference for the language or knowledge prior as well. To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt. More specifically, the framework performs an extra inference step in which a text prompt is introduced as an additional input. In this way, another preference is described by the generation probability of this extra inference process. The difference between the above two preferences, i.e. the difference between the probabilities, could be used as measurements for detecting factual inconsistencies. Interestingly, we found that with the properly designed prompt, our framework could evaluate specific preferences and serve as measurements for fine-grained categories of inconsistency, such as entity-related inconsistency, coreference-related inconsistency, etc. Moreover, our framework could also be extended to the supervised setting to learn better prompt from the labeled data as well. Experiments show that our framework achieves new SOTA results on three factual inconsistency detection tasks.