Papers by Nadav Oved
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains (2022.tacl-1)
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| Challenge: | Domain Adaptation (DA) algorithms suffer degradation when applied to out-of-distribution examples. |
| Approach: | They propose an example-based autoregressive Prompt learning algorithm for on-the-fly Any-Domain Adaptation . the algorithm is trained to generate a unique prompt that maps the test example to a semantic space . |
| Outcome: | The proposed model outperforms baselines in 14 multi-source adaptation scenarios. |
PASS: Perturb-and-Select Summarizer for Product Reviews (2021.acl-long)
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| Challenge: | Existing work on product reviews summarization focuses on generating concise, coherent and informative summaries, but this task is challenging. |
| Approach: | They propose a product reviews summarization task that employs a large pre-trained Transformer-based model and a method for ranking these summaries according to desired criteria. |
| Outcome: | The proposed system avoids the problem of self-contradiction by ranking the summaries according to desired criteria. |
On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method (2023.tacl-1)
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| Challenge: | Existing models show impressive results on a common CQA benchmark, but are they robust to domain, setting and domain? |
| Approach: | They propose a prompt-based history modeling approach that adds textual prompts directly to the text of a passage. |
| Outcome: | The proposed model is simple, easy to plug into practically any model and highly effective. |
Measuring the Robustness of NLP Models to Domain Shifts (2024.findings-emnlp)
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Nitay Calderon, Naveh Porat, Eyal Ben-David, Alexander Chapanin, Zorik Gekhman, Nadav Oved, Vitaly Shalumov, Roi Reichart
| Challenge: | Existing research on domain robustness (DR) relies on the Source Drop (SD) but lacks a complementary metric, a new study finds . |
| Approach: | They propose to use the Target Drop (TD) to measure domain DR . they use a DR benchmark consisting of 7 diverse tasks to measure both metrics . |
| Outcome: | The proposed model types excel in-domain, but few-shot LLMs often surpass them cross-domain showing better robustness. |