Papers by Doron Kukliansy
All You May Need for VQA are Image Captions (2022.naacl-main)
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| Challenge: | Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. |
| Approach: | They propose a method that automatically derives VQA examples at volume by leveraging existing image-caption annotations combined with neural models for textual question generation. |
| Outcome: | The proposed method improves state-of-the-art zero-shot accuracy by double digits and achieves robustness that lacks in the same model trained on human-annotated VQA data. |
TRUE: Re-evaluating Factual Consistency Evaluation (2022.naacl-main)
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Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, Yossi Matias
| Challenge: | Grounded text generation systems often generate factual inconsistencies, hindering their real-world applicability. |
| Approach: | They propose a method to assess factual consistency metrics on standardized texts . they recommend NLI and question generation-and-answering-based methods as starting points . |
| Outcome: | The proposed method is more actionable and interpretable than previous methods. |