Papers by Weiting Tan
Condensing Multilingual Knowledge with Lightweight Language-Specific Modules (2023.emnlp-main)
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| Challenge: | Existing methods to boost performance in multilingual models but scalability is difficult to manage. |
| Approach: | They propose a method that incorporates language-specific (LS) modules to boost model performance. |
| Outcome: | The proposed method outperforms state-of-the-art methods while outperforming existing methods. |
Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation (2025.findings-emnlp)
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| Challenge: | AVLM integrates full-face visual cues into a pre-trained expressive speech model. |
| Approach: | They propose an Audio-Visual Language Model (AVLM) for expressive speech generation by integrating full-face visual cues into a pre-trained expressive speech model. |
| Outcome: | The proposed model incorporates full-face visual cues into a pre-trained expressive speech model. |
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles (2024.findings-naacl)
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Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, Yunmo Chen
| Challenge: | Recent work shows that large language models can generalize to machine translation using zero-shot examples with in-context learning. |
| Approach: | They investigate the factors contributing to this gap by matching the writing styles of the target corpus. |
| Outcome: | The proposed methods can be enhanced without the need for parallel demonstration examples. |
Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency (2023.findings-emnlp)
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| Challenge: | Manually "engineering" prompts for large language models can be laborious and time-intensive. |
| Approach: | They propose a new metric to quantify the expected utility of a language prompt. |
| Outcome: | The proposed metric outperforms previous prompt selection metrics with 10% increase in Pearson correlation across 6 classification benchmarks and the prompt selected by the proposed meter gains 5% higher accuracy than previous metrics. |
Multilingual Representation Distillation with Contrastive Learning (2023.eacl-main)
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| Challenge: | Contextual representations from large pretrained language models encode semantic information from two or more languages. |
| Approach: | They integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences. |
| Outcome: | The proposed model outperforms existing models with similarity searches and filtering tasks across low-resource languages. |
Upsample or Upweight? Balanced Training on Heavily Imbalanced Datasets (2025.naacl-long)
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| Challenge: | a lack of data across domains creates significant imbalances in training data sizes . a recent study shows that temperature sampling and scaling are equivalent but differ under stochastic gradient descent due to differences in gradient variance. |
| Approach: | They propose a method that upsamples low-resource languages and upweights their loss functions to address this disparity. |
| Outcome: | The proposed method competes effectively with existing data re-weighting techniques while offering computational efficiency. |
The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)
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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi
| Challenge: | Recent studies show that malicious prompt instructions could solicit objectionable content from LLMs. |
| Approach: | They compare how state-of-the-art LLMs respond to malicious prompts in different languages . they find that LLM's generate unsafe responses more often when a prompt is written in a lower-resource language . |
| Outcome: | The proposed model can generate unsafe responses more often when a malicious prompt is written in a lower-resource language, and less irrelevant responses when written in lower-source languages. |