Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where truth is evolving and technically complex. We introduce CaseFacts, a benchmark for verifying colloquial legal claims against U.S. Supreme Court precedents. Unlike existing resources that map formal texts to formal texts, CaseFacts challenges systems to bridge the semantic gap between layperson assertions and technical jurisprudence while accounting for temporal validity. The dataset consists of 6,294 claims categorized as Supported, Refuted, or Overruled. We construct this benchmark using a multi-stage pipeline that leverages Large Language Models (LLMs) to synthesize claims from expert case summaries, employing a novel semantic similarity heuristic to efficiently identify and verify complex legal overrulings. Experiments with state-of-the-art LLMs reveal that the task remains challenging; notably, augmenting models with unrestricted web search degrades performance compared to closed-book baselines due to the retrieval of noisy, non-authoritative precedents. We release CaseFacts to spur research into legal fact verification systems.

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 introduce ClaimCheck, an efficient fact-checking system that verifies textual claims using smaller, open-source large language models. ClaimCheck integrates two fact-checking strategies, claim-matching and novel claim processing. Claim-matching uses related fact-checks from trusted organizations to fact-check a claim. Novel claim processing breaks down fact-checking into manageable subtasks—generating targeted questions, retrieving Web evidence, extracting answers, and synthesizing verdicts. Evaluation on the AVeriTeC benchmark demonstrates 62.6% verdict prediction accuracy, with claim-matching providing a 2.8% improvement. ClaimCheck approaches the performance of state-of-the-art systems while requiring significantly fewer computational resources, demonstrating the effectiveness of using small language models for fact-checking tasks. Furthermore, our code is publicly available to help make automated fact-checking more accessible.