Papers by David Arbour
Image Difference Captioning via Adversarial Preference Optimization (2025.emnlp-main)
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| Challenge: | Existing supervised approaches to image difference captioning overfit to dataset-specific language patterns and fail to capture accurate preferences. |
| Approach: | They propose an adversarial direct preference optimization framework that aligns captioning policy with pairwise difference preferences via Direct Preference Optimization. |
| Outcome: | The proposed approach outperforms baselines on benchmark IDC datasets in generating fine-grained and accurate difference descriptions. |
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)
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| Challenge: | Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context. |
| Approach: | They propose principled content selection as a way to increase source coverage . they use determinantal point processes to prioritize diverse content . |
| Outcome: | The proposed method improves source coverage on the DiverseSumm benchmark. |