10 papers · 1 filter
Incentivizing Time-Aware Fairness in Data Sharing
Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim +4
In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the…
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…
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Zijian Zhou, Ao Qu, Zhaoxuan Wu +6
Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries.…
Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space
Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo +1
Large language models (LLMs) are used in chatbots or AI assistants to hold conversations with a human user. In such applications, the quality (e.g., user engagement, safety) of a c…
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…
TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding
Zhaoxuan Wu, Zijian Zhou, Arun Verma +3
We propose TETRIS, a novel method that optimizes the total throughput of batch speculative decoding in multi-request settings. Unlike existing methods that optimize for a single re…