Papers with LLM-distilled embedding models
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. |