⚖️ Beyond True or False: Retrieval‑Augmented Hierarchical Analysis of Nuanced Claims
We introduce ClaimSpect, a framework that moves beyond binary fact-checking by constructing a hierarchical tree of aspects and sub-aspects relevant to a claim—grounded in evidence retrieved from a target corpus. It helps provide insight into the overall perspectives towards nuanced claims that cannot be easily verified, identifying the skew of perspectives and which aspects or sub-aspects do not have consensus behind them.
🔍 Nuance over Binary – Challenges the oversimplification of claims into “true/false,” proposing a deeper, aspect-based breakdown (efficacy, safety, logistics…)
🌳 Hierarchical Lens on Claims – Breaks down each claim into a tree of aspects and sub-aspects (e.g., Safety → Side Effects, Long-Term Risks), enabling a structured, multi-faceted analysis grounded in the retrieved corpus
📚 Corpus-aware Perspectives – Retrieves text snippets to uncover supportive, neutral, or opposing evidence, and quantifies prevalence (e.g., “how many studies support transportability?”)
🧪 Cross-Domain Validation – Applies this method to both scientific and political claims, showing strong performance across diverse domains
✅ Human & Baseline Benchmarks – Outperforms multiple baselines, with human evaluation confirming its strength in surfacing structured, nuanced insights