1 citations · 2 across the 9 of their papers we have counts for
9 papers
Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering
Hieu Nguyen, Mahammed Kamruzzaman, Anshuman Chhabra +1
Sycophancy refers to the tendency for large language models (LLMs) to match user beliefs at the cost of factual correctness, thereby undermining model reliability. Prior work on ev…
Towards Signboard-Oriented Visual Question Answering: ViSignVQA Dataset, Method and Benchmark
Hieu Minh Nguyen, Tam Le-Thanh Dang, Kiet Van Nguyen
Understanding signboard text in natural scenes is essential for real-world applications of Visual Question Answering (VQA), yet remains underexplored, particularly in low-resource…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Evaluating Precise Geolocation Inference Capabilities of Vision Language Models
Neel Jay, Hieu Minh Nguyen, Trung Dung Hoang +1
The prevalence of Vision-Language Models (VLMs) raises important questions about privacy in an era where visual information is increasingly available. While foundation VLMs demonst…
Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation
Hieu Nguyen, Zihao He, Shoumik Atul Gandre +3
Large language models (LLMs) often suffer from hallucination, generating factually incorrect or ungrounded content, which limits their reliability in high-stakes applications. A ke…
A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks
Hieu Minh "Jord" Nguyen
Theory of Mind (ToM), the ability to attribute mental states to others and predict their behaviour, is fundamental to social intelligence. In this paper, we survey studies evaluati…
"A Woman is More Culturally Knowledgeable than A Man?": The Effect of Personas on Cultural Norm Interpretation in LLMs
Mahammed Kamruzzaman, Hieu Nguyen, Nazmul Hassan +1
As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is b…
"Global is Good, Local is Bad?": Understanding Brand Bias in LLMs
Mahammed Kamruzzaman, Hieu Minh Nguyen, Gene Louis Kim
Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different bran…
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Stephen Casper, Jieun Yun, Joonhyuk Baek +13
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying…