54 citations · 74 across the 6 of their papers we have counts for
6 papers
How Much Do LLMs Know About Chinese Zero Pronouns?
Yifei Li, Guanyi Chen, Tingting He
Zero Pronouns (ZPs) are a pervasive linguistic phenomenon in pro-drop languages such as Chinese and have long posed a challenge for natural language processing systems. Although La…
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
Xin Guo, Haotian Xia, Zhaowei Liu +14
Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been…
Flows: Building Blocks of Reasoning and Collaborating AI
Martin Josifoski, Lars Klein, Maxime Peyrard +8
Recent advances in artificial intelligence (AI) have produced highly capable and controllable systems. This creates unprecedented opportunities for structured reasoning as well as…
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
Miao Xiong, Zhiyuan Hu, Xinyang Lu +4
Empowering large language models to accurately express confidence in their answers is essential for trustworthy decision-making. Previous confidence elicitation methods, which prim…
A Survey on Knowledge-Enhanced Pre-trained Language Models
Chaoqi Zhen, Yanlei Shang, Xiangyu Liu +3
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs…
Conceptor-Aided Debiasing of Large Language Models
Li S. Yifei, Lyle Ungar, João Sedoc
Pre-trained large language models (LLMs) reflect the inherent social biases of their training corpus. Many methods have been proposed to mitigate this issue, but they often fail to…