Zobrazeno 1 - 10
of 301
pro vyhledávání: '"Chen, Jianshu"'
Autor:
Wang, Kuan, Bukharin, Alexander, Jiang, Haoming, Yin, Qingyu, Wang, Zhengyang, Zhao, Tuo, Shang, Jingbo, Zhang, Chao, Yin, Bing, Li, Xian, Chen, Jianshu, Li, Shiyang
Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models trained on open-source IFT datasets only have the ability to follow ins
Externí odkaz:
http://arxiv.org/abs/2409.13733
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 7, Pp 217-231 (2019)
We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our dat
Externí odkaz:
https://doaj.org/article/c7436586b70a450f9f864f20cc1da55e
Autor:
Zhao, Xinran, Zhang, Hongming, Pan, Xiaoman, Yao, Wenlin, Yu, Dong, Wu, Tongshuang, Chen, Jianshu
Publikováno v:
Findings of the Association for Computational Linguistics ACL 2024
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be th
Externí odkaz:
http://arxiv.org/abs/2402.17124
We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful and harmless AI systems. However, it is generally costly and unstable to fine-tune large foundation models usin
Externí odkaz:
http://arxiv.org/abs/2402.10207
Autor:
Liu, Fuxiao, Wang, Xiaoyang, Yao, Wenlin, Chen, Jianshu, Song, Kaiqiang, Cho, Sangwoo, Yacoob, Yaser, Yu, Dong
With the rapid development of large language models (LLMs) and their integration into large multimodal models (LMMs), there has been impressive progress in zero-shot completion of user-oriented vision-language tasks. However, a gap remains in the dom
Externí odkaz:
http://arxiv.org/abs/2311.10774
Autor:
Wu, Xuansheng, Yao, Wenlin, Chen, Jianshu, Pan, Xiaoman, Wang, Xiaoyang, Liu, Ninghao, Yu, Dong
Large Language Models (LLMs) have achieved remarkable success, where instruction tuning is the critical step in aligning LLMs with user intentions. In this work, we investigate how the instruction tuning adjusts pre-trained models with a focus on int
Externí odkaz:
http://arxiv.org/abs/2310.00492
We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by combining foundational skills, a critical reasoning ability akin to human
Externí odkaz:
http://arxiv.org/abs/2308.00304
Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. However, the existing information ret
Externí odkaz:
http://arxiv.org/abs/2307.10442
Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically hampers their reliability. In this work, we address this crucial prob
Externí odkaz:
http://arxiv.org/abs/2307.03987
We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. Different from the conventional closed-world setting of Information Extraction (IE), Open-world IE con
Externí odkaz:
http://arxiv.org/abs/2305.14898