6 citations · 8 across the 11 of their papers we have counts for
11 papers
Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume
Gregory Kang Ruey Lau, Hieu Dao, Nicole Kan Hui Lin +1
Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could…
Rethinking Bayesian Optimization for Co-Optimizing LLM Training Configurations
Zhiliang Chen, Alfred Wei Lun Leong, Shao Yong Ong +6
Fine-tuning an LLM to maximize performance on a downstream task requires finding the optimal data and model configuration. This is a black-box optimization problem that Bayesian op…
Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou +15
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented compl…
WaterDrum: Watermarking for Data-centric Unlearning Metric
Xinyang Lu, Xinyuan Niu, Gregory Kang Ruey Lau +7
Large language model (LLM) unlearning is critical in real-world applications where it is necessary to efficiently remove the influence of private, copyrighted, or harmful data from…
PIED: Physics-Informed Experimental Design for Inverse Problems
Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng +1
In many science and engineering settings, system dynamics are characterized by governing PDEs, and a major challenge is to solve inverse problems (IPs) where unknown PDE parameters…
DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
Zhiliang Chen, Gregory Kang Ruey Lau, Chuan-Sheng Foo +1
The performance of an LLM depends heavily on the relevance of its training data to the downstream evaluation task. However, in practice, the data involved in an unseen evaluation t…
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu +3
Large Language Models (LLMs), particularly smaller variants, still struggle with complex reasoning tasks. While inference-time prompting can guide reasoning, existing methods often…
Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs
Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao +3
Protecting intellectual property (IP) of text such as articles and code is increasingly important, especially as sophisticated attacks become possible, such as paraphrasing by larg…
Data-Centric AI in the Age of Large Language Models
Xinyi Xu, Zhaoxuan Wu, Rui Qiao +16
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making the key observation that data is instrumental in…
PINNACLE: PINN Adaptive ColLocation and Experimental points selection
Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng +1
Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints, train with a composite loss function that contains multiple training point types: different ty…
Quantum Bayesian Optimization
Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma +3
Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been the…