4 citations · 11 across the 6 of their papers we have counts for
6 papers
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang +15
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, a…
LLMs' Classification Performance is Overclaimed
Hanzi Xu, Renze Lou, Jiangshu Du +6
In many classification tasks designed for AI or human to solve, gold labels are typically included within the label space by default, often posed as "which of the following is corr…
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
Hanzi Xu, Muhao Chen, Lifu Huang +2
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often…
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
Renze Lou, Kai Zhang, Jian Xie +5
In the realm of large language models (LLMs), enhancing instruction-following capability often involves curating expansive training data. This is achieved through two primary schem…
Robustness of Learning from Task Instructions
Jiasheng Gu, Hongyu Zhao, Hanzi Xu +3
Traditional supervised learning mostly works on individual tasks and requires training on a large set of task-specific examples. This paradigm seriously hinders the development of…
OpenStance: Real-world Zero-shot Stance Detection
Hanzi Xu, Slobodan Vucetic, Wenpeng Yin
Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations:…