Effectiveness of Context Augmentation Beyond Evaluated Domains

Determine whether augmenting verifiable claim detection with externally retrieved and summarized context via the ContextClaim paradigm improves performance in domains beyond the CT22 COVID-19 tweets and PoliClaim political speeches datasets, particularly in domains with differing entity distributions or linguistic characteristics.

Background

The paper introduces ContextClaim, a context-driven approach to verifiable claim detection that extracts entities from claims, retrieves relevant Wikipedia content, summarizes it using LLMs, and feeds the summary into downstream classifiers. The authors evaluate the approach on two datasets—CT22 (COVID-19 tweets) and PoliClaim (political speech)—across encoder-only and decoder-only architectures in fine-tuning, zero-shot, and few-shot settings.

While the experiments show that context augmentation often improves detection, the evaluation is limited to these two domains. The authors explicitly note that assessing effectiveness in domains with different entity distributions or linguistic characteristics is not yet addressed, leaving generalization to other domains as an unresolved question.