Papers by Wei-Rui Chen
Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation (2026.findings-acl)
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Wei-Rui Chen, Vignesh Kothapalli, Ata Fatahibaarzi, Hejian Sang, Shao Tang, Qingquan Song, Zhipeng Wang, Muhammad Abdul-Mageed
| Challenge: | a large reasoning model (LRM) training on large amounts of reasoning data is computationally expensive. |
| Approach: | They propose a method to quantify computation-quality tradeoffs as a function of sequence length. |
| Outcome: | The proposed method reduces training time, memory and FLOPs by 50% on long training sequences while retaining the full-sequence performance. |
Fumbling in Babel: An Investigation into ChatGPT’s Language Identification Ability (2024.findings-naacl)
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| Challenge: | ChatGPT is a powerful NLP tool but its language identification abilities are unclear. |
| Approach: | They compile a benchmark comprising 670 languages representing 23 language families spoken in five continents and compare their language identification abilities to ChatGPT's (both GPT-3.5 and GPT-4) performance. |
| Outcome: | The proposed model performs poorly on African languages, while GPT-3.5 and GPT-4 perform poorly on English, Afrikaans, Arabic, Indonesian, Italian, Mandarin Chinese, and several more. |
Interplay of Machine Translation, Diacritics, and Diacritization (2024.naacl-long)
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| Challenge: | MT and diacritization influence performance in a multi-task learning setting, but keeping diacritics is harmful for some languages. |
| Approach: | They propose two classes of metrics to measure the complexity of a diacritical system and propose to use them to compare performance. |
| Outcome: | The proposed metrics correlate positively with the performance of the models. |