Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? (2024.emnlp-main)
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Alexander Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
| Challenge: | a study of multilingual pre-trained LLMs on parallel instruction-tuning benchmarks shows that instruction-following models can be used across languages by up to 9.9%. |
| Approach: | They conduct an extensive study of the performance of multilingual pre-trained LLMs instruction-tuned on parallel instruction-uning datasets. |
| Outcome: | The proposed model improves cross-lingual instruction following capabilities by 9.9% on a large and mid-sized LLM on parallel instruction-tuning datasets. |
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Jerry Huang, Peng Lu, Qiuhao Zeng, Yusuke Iwasawa, Yutaka Matsuo, Sarath Chandar, Edison Marrese-Taylor, Irene Li
| Challenge: | despite advances in foundation model research, the relationship between large language models and their calibration remains an open area of research. |
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An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)
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| Challenge: | emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage. |
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