Papers by Zhaolin Li
Multimodal In-context Learning for ASR of Low-resource Languages (2026.findings-acl)
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| Challenge: | In-context learning with large language models addresses this limitation, but prior work focuses on high-resource languages covered during training and text-only settings. |
| Approach: | They propose to use multimodal ICL to learn unseen languages with multimodal learning to improve ASR in large language models. |
| Outcome: | The proposed model outperforms existing models on unseen languages with multimodal ICL (MICL) and cross-lingual transfer learning matches or outperformed models without using target-language data. |
Speech Recognition Corpus of the Khinalug Language for Documenting Endangered Languages (2024.lrec-main)
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| Challenge: | Existing tools to document endangered languages are limited due to data scarcity and the need for training. |
| Approach: | They propose to use a speech corpus for Khinalug, an endangered language spoken in northern Azerbaijan, to create a model that can be used in language documentation scenarios. |
| Outcome: | The proposed model achieves 6.65 CER points and 25.53 WER points in low-resource scenarios. |
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation (2023.emnlp-demo)
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Christian Huber, Tu Anh Dinh, Carlos Mullov, Ngoc-Quan Pham, Thai Binh Nguyen, Fabian Retkowski, Stefan Constantin, Enes Ugan, Danni Liu, Zhaolin Li, Sai Koneru, Jan Niehues, Alexander Waibel
| Challenge: | a framework to evaluate low-latency speech translations is currently only limited to specific aspects and is not able to compare different approaches. |
| Approach: | They propose a framework to perform and evaluate low-latency speech translation in realistic conditions. |
| Outcome: | The proposed framework evaluates various aspects of low-latency speech translation under realistic conditions. |
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading (2024.emnlp-main)
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Tu Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues
| Challenge: | Large Language Models (LLMs) are rapidly developing and are becoming more and more useful in scientific tasks. |
| Approach: | They propose to use LLM-as-a-judge to grade LLMs on SciEx to assess their ability on scientific tasks. |
| Outcome: | The proposed benchmarks show that the LLMs perform decently on free-form exams, achieving 0.948 Pearson correlation with expert grading. |