1 citations · 1 across the 2 of their papers we have counts for
3 papers
Self-Improvement as Coherence Optimization: A Theoretical Account
Tianyi Qiu, Ahmed Hani Ismail, Zhonghao He +1
Can language models improve their accuracy without external supervision? Methods such as debate, bootstrap, and internal coherence maximization achieve this surprising feat, even m…
Martingale Score: An Unsupervised Metric for Bayesian Rationality in LLM Reasoning
Zhonghao He, Tianyi Qiu, Hirokazu Shirado +1
Recent advances in reasoning techniques have substantially improved the performance of large language models (LLMs), raising expectations for their ability to provide accurate, tru…
The Lock-in Hypothesis: Stagnation by Algorithm
Tianyi Alex Qiu, Zhonghao He, Tejasveer Chugh +1
The training and deployment of large language models (LLMs) create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated co…