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Learning to poison large language models during instruction tuning
Y Qiang, X Zhou, SZ Zade, MA Roshani… - ar** public opinion. Despite journalism's aim for
impartial reporting, various biases can emerge during writing and publication phases. While …
impartial reporting, various biases can emerge during writing and publication phases. While …
[HTML][HTML] Harnessing Response Consistency for Superior LLM Performance: The Promise and Peril of Answer-Augmented Prompting
This paper introduces Answer-Augmented Prompting (AAP), an innovative approach that
leverages the Response Consistency of History of Dialogue (HoD) phenomenon in Large …
leverages the Response Consistency of History of Dialogue (HoD) phenomenon in Large …
Intended Target Identification for Anomia Patients with Gradient-based Selective Augmentation
In this study, we investigate the potential of language models (LMs) in aiding patients
experiencing anomia, a difficulty identifying the names of items. Identifying the intended …
experiencing anomia, a difficulty identifying the names of items. Identifying the intended …
Hijacking Large Language Models via Adversarial In-Context Learning
Y Qiang - 2024 - search.proquest.com
In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific
downstream tasks by utilizing labeled examples as demonstrations in the precondition …
downstream tasks by utilizing labeled examples as demonstrations in the precondition …
[PDF][PDF] On Learning Frequency-Instance Correlations by Model-Agnostic Training for Synthetic Speech Detection
Abstract The goal of Synthetic Speech Detection (SSD) is to detect spoofing speech
synthesized by text-to-speech and voice conversion. Most existing SSD methods focus only …
synthesized by text-to-speech and voice conversion. Most existing SSD methods focus only …