6 papers · 1 filter
Communicating Chess Strategies in Natural Language
Langyuan Cui, Chun Kai Ling, Hwee Tou Ng
Chess engines have long achieved superhuman playing strength. However, the underlying strategy behind their move suggestions is difficult for human players, even skilled ones, to c…
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…
Preference-Guided Reflective Sampling for Aligning Language Models
Hai Ye, Hwee Tou Ng
Iterative data generation and model re-training can effectively align large language models(LLMs) to human preferences. The process of data sampling is crucial, as it significantly…