Frame-semantic parsing is a critical task in natural language understanding, yet the ability of large language models (LLMs) to extract frame-semantic arguments remains underexplored. This paper presents a comprehensive evaluation of LLMs on frame-semantic argument identification, analyzing the impact of input representation formats, model architectures, and generalization to unseen and out-of-domain samples. Our experiments, spanning models from 0.5B to 72B parameters, reveal that JSON-based representations significantly enhance performance, and while larger models generally perform better, smaller models can achieve competitive results through fine-tuning. We also introduce a novel approach to frame identification leveraging predicted frame elements, achieving state-of-the-art performance on ambiguous targets. Despite strong generalization capabilities, our analysis finds that LLMs still struggle with out-of-domain data.

We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them. To support this, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames based on data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval and explainability for fact-checking. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future work in this direction.

We present ClaimLens, an automated fact-checking system focused on voting-related factual claims. Existing fact-checking solutions often lack transparency, making it difficult for users to trust and understand the reasoning behind the outcomes. In this work, we address the critical need for transparent and explainable automated fact-checking solutions. We propose a novel approach that leverages frame-semantic parsing to provide structured and interpretable fact verification. By focusing on voting-related claims, we can utilize publicly available voting records from official United States congressional sources and the established Vote semantic frame to extract relevant information from claims. Furthermore, we propose novel data augmentation techniques for frame-semantic parsing, a task known to lack robust annotated data, which leads to a +9.5% macro F1 score on frame element identification over our baseline.