Papers by Jabez Magomere
FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking (2025.findings-naacl)
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| Challenge: | FinNLI is a benchmark dataset for Financial Natural Language Inference (NLI) across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transcripts. |
| Approach: | They propose to use FinNLI to evaluate financial natural language inference models across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transcripts. |
| Outcome: | The proposed dataset is based on a high-quality test set of 3,304 instances annotated by finance experts. |
Distill and Align Decomposition for Enhanced Claim Verification (2026.findings-eacl)
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Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay, Charese Smiley, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing methods for complex claim verification struggle to align decomposition quality with verification performance. |
| Approach: | They propose a reinforcement learning approach that optimizes decomposition quality and verifier alignment using Group Relative Policy Optimization. |
| Outcome: | The proposed method outperforms prompt-based approaches and existing methods in six evaluation settings. |
A Variational Approach for Mitigating Entity Bias in Relation Extraction (2025.acl-short)
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| Challenge: | Relation Extraction (RE) models often rely excessively on entities, resulting in poor generalization. |
| Approach: | They propose a Variational Information Bottleneck (VIB) framework to reduce entity bias in Relation Extraction (RE) . their method extracts relational information from unstructured data to improve generalization . |
| Outcome: | The proposed method achieves state-of-the-art on general and financial domain RE datasets, excelling in in-domain settings and out-of domain. |
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits (2025.findings-acl)
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| Challenge: | Existing claims-matching systems that use sentence embedding models are not robust to edits as users interact with claims online. |
| Approach: | They propose a perturbation framework that generates valid and natural claim variations and evaluate different mitigation approaches to improve their findings. |
| Outcome: | The proposed framework evaluates embedding models in a multi-stage retrieval pipeline and identifies the effectiveness of mitigation approaches. |