Di Wu
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Hi there! I’m Di Wu 吴迪, a Phd candidate (starting in Sept 2022) under the supervision of Christof Monz
at Language Technology Lab, University of Amsterdam.
I appreciate designing models or approaches that are driven by intuition after capturing deep understandings of specific
fields, equipped with modern neural architectures, and grounded in real-world scenarios. The research that attracted me
most is simple, insightful, and far-reaching creations or findings, such as Word2Vec.
I mainly focus on Machine Translation, having strong interests in tokenization, quality estimation, and the knowledge transfer mechanism. Beyond translation, I am drawn to fundamental problems in NLP/ML that appear counterintuitive or conceptually puzzling.
Here are some problems I get interested in now. If you’re willing to chat about them, leave me a message. I’m always
open to collaborations, or any kind of chat.
Selected Papers
- Calibrating Translation Decoding with Quality Estimation on LLMs
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We unify quality optimization and estimation in translation, achieving both SOTA translation
performance and accurate quality estimation within a single model.
- Di Wu, Yibin Lei, Christof Monz; arXiv25, [PDF]
- Two Simple Experiments on Whether Human-Like Reasoning Helps Translation
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We use two simple experiments to question the trend of using CoT for translation.
- Di Wu, Seth Aycock, Christof Monz; arXiv25, [PDF]
- Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?
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We show no evidence that long-context LLMs can make use of grammatical explanations for translation, challenging the setting of
MT from One Book.
- Seth Aycock, David Stap, Di Wu, Christof Monz, Khalil Sima’an; ICLR25, [PDF]
- Representational Isomorphism and Alignment of Multilingual Large Language Models
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We show that the representation of LLMs shares a high degree of isomorphism across languages, providing foundations for zero- or few-shot learning for cross-lingual tasks.
- Di Wu, Yibin Lei, Andrew Yates, Christof Monz; EMNLP24 Findings, [PDF]
- How Far can 100 Samples Go? Unlocking Zero-Shot Translation with Tiny Multi-Parallel Data
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Pragmatic views on zero-shot MT: (1) Its potential is underestimated, tiny data brings big gains; (2) The off-target issue is overestimated, one single example can resolve it.
- Di Wu, Shaomu Tan, Yan Meng, David Stap, Christof Monz; ACL24 Findings, [PDF]
- Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation
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We reparameterize the embedding table to overcome the barriers of writing scripts across languages, encouraging positive knowledge transfer.
- Di Wu, Christof Monz; EMNLP23, [PDF]
You may know me better from this personal page.