Papers by David Stap
How Far can 100 Samples Go? Unlocking Zero-Shot Translation with Tiny Multi-Parallel Data (2024.findings-acl)
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| Challenge: | a common solution to zero-shot translation is to add as many related translation directions as possible to the training corpus. |
| Approach: | They show that a small amount of multi-parallel data can achieve significant zero-shot improvements . they say that the resulting non-English performance is close to the complete translation upper bound . |
| Outcome: | The proposed model achieves +21.7 ChrF++ non-English translation improvements on EC30 dataset . the resulting non- English performance exceeds M2M100 by an average of 5.9 ChrF+ . |
Multilingual k-Nearest-Neighbor Machine Translation (2023.emnlp-main)
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| Challenge: | knearest-neighbor machine translation has shown remarkable improvements in machine translation quality by creating a datastore of cached examples. |
| Approach: | They combine k-nearest-neighbor machine translation with a datastore of cached examples to achieve significant improvements in machine translation quality. |
| Outcome: | The proposed approach improves translation quality by 5.3x in multilingual datastores, while limiting performance to low-resource languages. |
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens (2023.findings-emnlp)
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| Challenge: | Existing studies show that translation quality alone is not sufficient for measuring knowledge transfer in multilingual neural machine translation. |
| Approach: | They propose a method that measures representational similarities between languages to measure knowledge transfer. |
| Outcome: | The proposed method improves translation quality for low- and mid-resource languages across multiple datasets and models. |
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities (2024.acl-long)
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| Challenge: | Recent studies have shown that fine-tuning large language models improves their translations, but it is unclear what is the impact on desirable LLM behaviors that are not present in neural machine translation models. |
| Approach: | They perform an extensive translation evaluation on LLaMA and Falcon models with model size ranging from 7 billion up to 65 billion parameters. |
| Outcome: | The proposed model produces less literal translations after fine-tuning on parallel data. |
The Effect of Language Diversity When Fine-Tuning Large Language Models for Translation (2025.findings-emnlp)
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| Challenge: | Prior research on language diversity in LLM fine-tuning has reported benefits while others find no benefits. |
| Approach: | They find that expanding language diversity during fine-tuning improves translation quality . they also show that increased language diversity creates more language-agnostic representations . |
| Outcome: | The proposed model improves translation quality for unsupervised and supervised pairs . the results plateau or decrease beyond a certain diversity threshold. |