Papers by Thomas Wu
SwiLTra-Bench: The Swiss Legal Translation Benchmark (2025.acl-long)
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Joel Niklaus, Jakob Merane, Luka Nenadic, Sina Ahmadi, Yingqiang Gao, Cyrill A. H. Chevalley, Claude Humbel, Christophe Gösken, Lorenzo Tanzi, Thomas Lüthi, Stefan Palombo, Spencer Poff, Boling Yang, Nan Wu, Matthew Guillod, Robin Mamié, Daniel Brunner, Julio Pereyra, Niko Grupen
| Challenge: | In Switzerland legal translation relies on legal experts who must be both legal experts and skilled translators—creating bottlenecks and impacting effective access to justice. |
| Approach: | They propose a multilingual benchmarking system that evaluates Swiss legal translation systems based on 180K aligned Swiss legal translator pairs . they show frontier models achieve superior translation performance across all document types while specialized translation systems excel specifically in laws but under-perform in headnotes. |
| Outcome: | The proposed model outperforms specialized models in laws but underperform in headnotes. |
Detecting and Mitigating Challenges in Zero-Shot Video Summarization with Video LLMs (2025.findings-acl)
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Luca Cagliero, Lorenzo Vaiani, Eliana Pastor, Alkis Koudounas, Elena Baralis, Vittorio Mazzia, Sandro Pollastrini, Thomas Gueudre, Manuel Giollo, Daniele Amberti, Yue Wu
| Challenge: | Video Large Language Models (VLLMs) exhibit impressive zero-shot capabilities in video analysis, but their performance varies significantly depending on the LLM prompt, the characteristics of the video, and the properties of the training data and LLM architecture. |
| Approach: | They propose to use Chain-of-Thought prompting to inject knowledge extracted by external, lightweight models into video summarization benchmarks to evaluate their performance. |
| Outcome: | The proposed solutions improve summarization performance by injecting knowledge extracted by external, lightweight models. |
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media (2025.findings-naacl)
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Owen Cook, Charlie Grimshaw, Ben Peng Wu, Sophie Dillon, Jack Hicks, Luke Jones, Thomas Smith, Matyas Szert, Xingyi Song
| Challenge: | Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people. |
| Approach: | They propose to use inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability. |
| Outcome: | The proposed framework utilises inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability. |
Causal Distillation for Language Models (2022.naacl-main)
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Zhengxuan Wu, Atticus Geiger, Joshua Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah Goodman
| Challenge: | Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. |
| Approach: | They propose to augment distillation with a third objective that encourages the student model to imitate the causal dynamics of the teacher through a distillation interchange intervention training objective (DIITO). |
| Outcome: | The proposed method lowers perplexity on the WikiText-103M corpus and improves on the GLUE benchmark, SQuAD, and CoNLL-2003. |
Dual-Path Dynamic Fusion with Learnable Query for Multimodal Sentiment Analysis (2025.emnlp-main)
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| Challenge: | Existing methods for multimodal sentiment analysis struggle with global and fine-grained contributions and over-reliance on text. |
| Approach: | They propose a multimodal sentiment analysis architecture that processes inputs through two complementary paths: global and local. |
| Outcome: | The proposed architecture achieves state-of-the-art in fine-grained sentiment prediction on the CMU-MOSI and CMU MOSEI benchmarks. |