Papers by Jacob Devasier
CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval (2026.acl-long)
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| Challenge: | Automated Fact-Checking has largely focused on verifying general knowledge against static corpora. |
| Approach: | They propose a benchmark to verify colloquial legal claims against Supreme Court precedents . the benchmark leverages large language models to synthesize claims from expert case summaries . they say the benchmark is a step forward in the field of legal fact verification . |
| Outcome: | The proposed benchmark bridges the gap between layperson assertions and technical jurisprudence while accounting for temporal validity. |
Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples (2024.acl-long)
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| Challenge: | Using a RoBERTa-based filter, we achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012. |
| Approach: | They propose a new prefix tree modification to enable robust support for multi-word lexical units and a RoBERTa-based filter to achieve an F1 score of 0.775. |
| Outcome: | The proposed model achieves an F1 score of 0.775, surpassing the state-of-the-art model by +0.012. |
Task-Oriented Automatic Fact-Checking with Frame-Semantics (2025.findings-acl)
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| Challenge: | Existing work on automatic fact-checking relies on unstructured data and large language models to produce fact- check verdicts and explanations. |
| Approach: | They propose a new paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact- checking them. |
| Outcome: | The proposed paradigm improves evidence retrieval and explainability for fact-checking by leveraging frame semantics. |
Can LLMs Extract Frame-Semantic Arguments? (2025.emnlp-main)
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| Challenge: | Frame-semantic parsing is a critical task in natural language understanding . however, the ability of large language models to extract frame-sensical arguments remains unexplored . |
| Approach: | They propose a framework to extract frame-semantic arguments from large language models . they use JSON representations to enhance performance, but smaller models can achieve competitive results . |
| Outcome: | The proposed model achieves state-of-the-art on ambiguous targets while limiting generalization to out-of domain data. |
ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics (2024.emnlp-demo)
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| Challenge: | Existing fact-checking solutions lack transparency and explainability . a lack of transparency can make it difficult for users to trust and understand the reasoning behind the outcomes. |
| Approach: | They propose an automated fact-checking system focused on voting-related factual claims that leverages frame-semantic parsing to provide structured and interpretable fact verification. |
| Outcome: | The proposed system can extract relevant information from voting-related factual claims using public records and Vote semantic frame. |