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cs.CL2024★ 1 cited
Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions
Kexun Zhang, Yee Man Choi, Zhenqiao Song +3
How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform we…
cs.CL2023★ 4 cited
INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback
Wenda Xu, Danqing Wang, Liangming Pan +4
Automatically evaluating the quality of language generation is critical. Although recent learned metrics show high correlation with human judgement, these metrics can not explain t…
cs.CL2021
MTG: A Benchmark Suite for Multilingual Text Generation
Yiran Chen, Zhenqiao Song, Xianze Wu +5
We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the la…