4 papers
Game of Thought: Robust Information Seeking with Large Language Models Using Game Theory
Langyuan Cui, Chun Kai Ling, Hwee Tou Ng
Large Language Models (LLMs) are increasingly deployed in real-world scenarios where they may lack sufficient information to complete a given task. In such settings, the ability to…
Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization
Yao Xiao, Hai Ye, Linyao Chen +4
Iterative data generation and model retraining are widely used to align large language models (LLMs). It typically involves a policy model to generate on-policy responses and a rew…
Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration
Hai Ye, Mingbao Lin, Hwee Tou Ng +1
Scaling laws for inference compute in multi-agent systems remain under-explored compared to single-agent scenarios. This work aims to bridge this gap by investigating the problem o…
Self-Judge: Selective Instruction Following with Alignment Self-Evaluation
Hai Ye, Hwee Tou Ng
Pre-trained large language models (LLMs) can be tailored to adhere to human instructions through instruction tuning. However, due to shifts in the distribution of test-time data, t…