257 citations · 369 across the 28 of their papers we have counts for
9 papers · 1 filter
Large Language Model for Science: A Study on P vs. NP
Qingxiu Dong, Li Dong, Ke Xu +4
In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer sci…
Extrapolating Large Language Models to Non-English by Aligning Languages
Wenhao Zhu, Yunzhe Lv, Qingxiu Dong +6
Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often strong…
Can Language Models Understand Physical Concepts?
Lei Li, Jingjing Xu, Qingxiu Dong +4
Language models~(LMs) gradually become general-purpose interfaces in the interactive and embodied world, where the understanding of physical concepts is an essential prerequisite.…
Can We Edit Factual Knowledge by In-Context Learning?
Ce Zheng, Lei Li, Qingxiu Dong +4
Previous studies have shown that large language models (LLMs) like GPTs store massive factual knowledge in their parameters. However, the stored knowledge could be false or out-dat…
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories
Heming Xia, Qingxiu Dong, Lei Li +4
Recently, Large Language Models (LLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge. However, it remains unclear…
Statistical Knowledge Assessment for Large Language Models
Qingxiu Dong, Jingjing Xu, Lingpeng Kong +2
Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? Existing LLMs may generate distinct responses for…
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
Wenhao Zhu, Hongyi Liu, Qingxiu Dong +5
Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages…
A Challenging Benchmark for Low-Resource Learning
Yudong Wang, Chang Ma, Qingxiu Dong +2
With promising yet saturated results in high-resource settings, low-resource datasets have gradually become popular benchmarks for evaluating the learning ability of advanced neura…
A Survey on In-context Learning
Qingxiu Dong, Lei Li, Damai Dai +10
With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make pre…