10 papers
Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition
Bo Su, Yueru Yan, Thai Le
We introduce target-speaker unlearning ASR (TSU-ASR) task in a fully end-to-end framework for multi-speaker ASR and diarization. Given a multi-speaker utterance and a set of opt-ou…
Locating and Controlling Implicit Personalization in Large Language Models
Yueru Yan, Siqi Wu, Thai Le
Large language models (LLMs) often shift their outputs in response to implicit demographic cues even when users never state a demographic identity. Previous work has documented thi…
PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
Bo Su, Ankit Shah, Thai Le
Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledg…
Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior
Tuc Nguyen, Thai Le
Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation vectors toward desired behav…
ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning
Tuc Nguyen, Thai Le
Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning…
ShareChat: A Dataset of Chatbot Conversations in the Wild
Yueru Yan, Tuc Nguyen, Bo Su +2
By evaluating Large Language Models (LLMs) through uniform, text-only interfaces, current academic benchmarks obscure how the unique designs and affordances of distinct commercial…
Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification
Tuc Nguyen, Yifan Hu, Thai Le
Recent advancements in large language models (LLMs) have been fueled by large scale training corpora drawn from diverse sources such as websites, news articles, and books. These da…
NoMatterXAI: Generating "No Matter What" Alterfactual Examples for Explaining Black-Box Text Classification Models
Tuc Nguyen, James Michels, Hua Shen +1
In Explainable AI (XAI), counterfactual explanations (CEs) are a well-studied method to communicate feature relevance through contrastive reasoning of "what if" to explain AI model…
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning
Tuc Nguyen, Thai Le
Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing…
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers
Tuc Nguyen, Thai Le
Existing works show that augmenting the training data of pre-trained language models (PLMs) for classification tasks fine-tuned via parameter-efficient fine-tuning methods (PEFT) u…