116 citations · 201 across the 10 of their papers we have counts for
10 papers
Does Prompt-Tuning Language Model Ensure Privacy?
Shangyu Xie, Wei Dai, Esha Ghosh +3
Prompt-tuning has received attention as an efficient tuning method in the language domain, i.e., tuning a prompt that is a few tokens long, while keeping the large language model f…
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
Hojjat Aghakhani, Wei Dai, Andre Manoel +7
With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massiv…
Planting and Mitigating Memorized Content in Predictive-Text Language Models
C. M. Downey, Wei Dai, Huseyin A. Inan +3
Language models are widely deployed to provide automatic text completion services in user products. However, recent research has revealed that language models (especially large one…
Fully and partially distributed Quantum Generalized Benders Decomposition for Unit Commitment Problems
Fang Gao, Dejian Huang, Ziwei Zhao +4
A series of hybrid quantum-classical generalized Benders decomposition (GBD) algorithms are proposed to address unit commitment (UC) problems under centralized, distributed, and pa…
Combing for Credentials: Active Pattern Extraction from Smart Reply
Bargav Jayaraman, Esha Ghosh, Melissa Chase +3
Pre-trained large language models, such as GPT\nobreakdash-2 and BERT, are often fine-tuned to achieve state-of-the-art performance on a downstream task. One natural example is the…
Solving DC Power Flow Problems Using Quantum and Hybrid algorithms
Fang Gao, Guojian Wu, Suhang Guo +2
Power flow calculation plays an important role in planning, operation, and control of the power system. The quantum HHL algorithm can achieve theoretical exponential speedup over c…