On Context-aware Detection of Cherry-picking in News Reporting
Cherry-picking refers to the deliberate selection of evidence or facts that favor a particular viewpoint while ignoring or distorting evidence that supports an opposing perspective. Manually identifying cherry-picked statements in news stories can be challenging. In this study, we introduce a novel approach to detecting cherry-picked statements by identifying missing important statements in a target news story using language models and contextual information from other news sources. Furthermore, this research introduces a novel dataset specifically designed for training and evaluating cherry-picking detection models. Our best performing model achieves an F-1 score of about 89% in detecting important statements. Moreover, results show the effectiveness of incorporating external knowledge from alternative narratives when assessing statement importance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On the Definition and Detection of Cherry-Picking in Counterfactual Explanations
Counterfactual explanations are widely used to communicate how inputs must change for a model to alter its prediction. For a single instance, many valid counterfactuals can exist, which leaves open the possibility for an…
On the existence of a cherry-picking sequence
Recently, the minimum number of reticulation events that is required to simultaneously embed a collection P of rooted binary phylogenetic trees into a so-called temporal network has been characterized in terms of cherry-…
Cherry on the Cake: Fairness is NOT an Optimization Problem
In Fair AI literature, the practice of maliciously creating unfair models that nevertheless satisfy fairness constraints is known as "cherry-picking". A cherry-picking model is a model that makes mistakes on purpose, sel…
FairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCherryRec: Enhancing News Recommendation Quality via LLM-driven Framework
Large Language Models (LLMs) have achieved remarkable progress in language understanding and generation. Custom LLMs leveraging textual features have been applied to recommendation systems, demonstrating improvements acr…
Movie RecommendationNews RecommendationRecommendation SystemsDeciding the existence of a cherry-picking sequence is hard on two trees
Here we show that deciding whether two rooted binary phylogenetic trees on the same set of taxa permit a cherry-picking sequence, a special type of elimination order on the taxa, is NP-complete. This improves on an earli…