Papers by Siwen Guo
Evaluating Parameter-Efficient Finetuning Approaches for Pre-trained Models on the Financial Domain (2023.findings-emnlp)
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| Challenge: | Large-scale language models with millions, billions, or trillions of trainable parameters are becoming increasingly popular. |
| Approach: | They compare performance of financial BERT-like models to their fully fine-tuned counterparts by using parameter-efficient tuning methods. |
| Outcome: | The proposed approaches match full fine-tuning performance on common NLP tasks, but are less studied in finance. |
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings (2025.coling-main)
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| Challenge: | Sentence embedding models are limited for many low-resource languages, including Luxembourgish. |
| Approach: | They propose to use Luxembourgish as an enhanced sentence embedding model with strong cross-lingual capabilities to address this issue. |
| Outcome: | The proposed model can embed Luxembourgish sentences better than high-resource languages. |
Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review (2023.acl-long)
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| Challenge: | Pre-trained Multilingual Language Models have shown a strong ability to transfer knowledge across languages. |
| Approach: | They examine factors contributing to the ability of MLLMs to perform zero-shot cross-lingual transfer . they identify consensuses among studies with consistent findings and resolve conflicts . |
| Outcome: | The authors outline and discuss factors that contribute to the ability of MLLMs to perform zero-shot cross-lingual transfer. |