Papers by Sedigheh Eslami
Diffusion-Pretrained Dense and Contextual Embeddings (2026.acl-industry)
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| Challenge: | pplx-embed uses diffusion-based pretraining to capture bidirectional context within passages. |
| Approach: | They propose a family of multilingual embedding models that leverage bidirectional attention through diffusion-based pretraining to capture bidirectional context within passages. |
| Outcome: | The proposed models achieve competitive performance on the MTEB(Multilingual, v2), MTEF(Code), BERGEN, and ToolRet retrieval benchmarks while pplx-embed-context-v1 sets new records on the ConTEB benchmark. |
PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain? (2023.findings-eacl)
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| Challenge: | Medical visual question answering is a multimodal task that requires a system to understand both medical images and textual questions and infer associations between them. |
| Approach: | They propose a fine-tuned version of CLIP for the medical domain based on PubMed articles. |
| Outcome: | The proposed model improves accuracy up to 3% on two MedVQA benchmark datasets. |