activity
20122023
most citedEnhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

1.1k citations · 1.5k across the 38 of their papers we have counts for

collaborators

39 papers

cs.CL2023★ 2 cited

Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection

Zekun Li, Baolin Peng, Pengcheng He +1

Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, becoming increasingly crucial across various applications. However, this capability…

cs.CL2023★ 33 cited

Augmenting Language Models with Long-Term Memory

Weizhi Wang, Li Dong, Hao Cheng +4

Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit, preventing them from utilizing rich long-context information from past inputs.…

cs.CL2023

Graph Reasoning for Question Answering with Triplet Retrieval

Shiyang Li, Yifan Gao, Haoming Jiang +5

Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which ar…

cs.CL2023★ 7 cited

Bot or Human? Detecting ChatGPT Imposters with A Single Question

Hong Wang, Xuan Luo, Weizhi Wang +1

Large language models (LLMs) like GPT-4 have recently demonstrated impressive capabilities in natural language understanding and generation. However, there is a concern that they c…

cs.LG2023★ 14 cited

Time Series as Images: Vision Transformer for Irregularly Sampled Time Series

Zekun Li, Shiyang Li, Xifeng Yan

Irregularly sampled time series are increasingly prevalent, particularly in medical domains. While various specialized methods have been developed to handle these irregularities, e…

cs.CL2023

Language Model Detoxification in Dialogue with Contextualized Stance Control

Jing Qian, Xifeng Yan

To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (…