We propose using prompts made up of multiple problems to evaluate LLM capabilities, an approach we call multi-problem evaluation. We examine 7 LLMs on 4 related task types constructed from 6 existing classification benchmarks. We find that while LLMs can generally perform multiple homogeneous classifications at once (Batch Classification) as well as when they do so separately, they perform significantly worse on two selection tasks that are conceptually equivalent to Batch Classification and involve selecting indices of text falling into each class label, either independently or altogether. We show that such a significant performance drop is due to LLMs’ inability to adequately combine index selection with text classification. Such a drop is surprisingly observed across all LLMs attested, under zero-shot, few-shot, and CoT settings, and even with a novel synthetic dataset, potentially reflecting an inherent capability limitation with modern LLMs.

We propose a novel clustering pipeline to detect and characterize influence campaigns from documents. This approach clusters parts of document, detects clusters that likely reflect an influence campaign, and then identifies documents linked to an influence campaign via their association with the high-influence clusters. Our approach outperforms both the direct document-level classification and the direct document-level clustering approach in predicting if a document is part of an influence campaign. We propose various novel techniques to enhance our pipeline, including using an existing event factuality prediction system to obtain document parts, and aggregating multiple clustering experiments to improve the performance of both cluster and document classification. Classifying documents after clustering not only accurately extracts the parts of the documents that are relevant to influence campaigns, but also captures influence campaigns as a coordinated and holistic phenomenon. Our approach makes possible more fine-grained and interpretable characterizations of influence campaigns from documents.