7 citations · 14 across the 7 of their papers we have counts for
7 papers
Uncertainty Quantification for In-Context Learning of Large Language Models
Chen Ling, Xujiang Zhao, Xuchao Zhang +10
In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the…
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
Chen Ling, Xuchao Zhang, Xujiang Zhao +7
Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional…
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Chen Ling, Xujiang Zhao, Xuchao Zhang +8
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples. Despite the potenti…
Adaptation Speed Analysis for Fairness-aware Causal Models
Yujie Lin, Chen Zhao, Minglai Shao +2
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the trai…
Multidimensional Uncertainty Quantification for Deep Neural Networks
Xujiang Zhao
Deep neural networks (DNNs) have received tremendous attention and achieved great success in various applications, such as image and video analysis, natural language processing, re…
Dynamic Prompting: A Unified Framework for Prompt Tuning
Xianjun Yang, Wei Cheng, Xujiang Zhao +3
It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language m…