activity
20162024
most citedPosition Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems

50 citations · 241 across the 24 of their papers we have counts for

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Showing cs.CLShow all

28 papers · 1 filter

cs.CL2023

Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection

Jun Yan, Vikas Yadav, Shiyang Li +6

Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions.…

cs.CL20213 cited

AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding

Jun Yan, Nasser Zalmout, Yan Liang +3

Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, wi…

cs.CL20211 cited

X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering

Meryem M'hamdi, Doo Soon Kim, Franck Dernoncourt +3

Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization…

cs.CL2021

On the Influence of Masking Policies in Intermediate Pre-training

Qinyuan Ye, Belinda Z. Li, Sinong Wang +5

Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline. Prior work has shown that inserting an intermediate pre-training stage, using…

cs.CL2021

CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP

Qinyuan Ye, Bill Yuchen Lin, Xiang Ren

Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how…

cs.CL2021

Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation

Yuning Mao, Wenchang Ma, Deren Lei +2

Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, wit…