9 papers
ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
Xiaoqiang Lin, Arun Verma, Zhongxiang Dai +3
The recent success in using human preferences to align large language models (LLMs) has significantly improved their performance in various downstream tasks, such as question answe…
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…
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…
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
Yao Shu, Wenyang Hu, See-Kiong Ng +2
Large Language Models (LLMs) have become indispensable in numerous real-world applications. However, fine-tuning these models at scale, especially in federated settings where data…
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…
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai +5
Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream task…