1 citations · 1 across the 8 of their papers we have counts for
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Response Time Enhances Alignment with Heterogeneous Preferences
Federico Echenique, Alireza Fallah, Baihe Huang +1
Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that…
Towards Anytime-Valid Statistical Watermarking
Baihe Huang, Eric Xu, Kannan Ramchandran +2
The proliferation of Large Language Models (LLMs) necessitates efficient mechanisms to distinguish machine-generated content from human text. While statistical watermarking has eme…
Sample Complexity and Representation Ability of Test-time Scaling Paradigms
Baihe Huang, Shanda Li, Tianhao Wu +5
Test-time scaling paradigms have significantly advanced the capabilities of large language models (LLMs) on complex tasks. Despite their empirical success, theoretical understandin…
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics
Hanlin Zhu, Baihe Huang, Shaolun Zhang +4
Auto-regressive large language models (LLMs) show impressive capacities to solve many complex reasoning tasks while struggling with some simple logical reasoning tasks such as inve…
DAVED: Data Acquisition via Experimental Design for Data Markets
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy +3
The acquisition of training data is crucial for machine learning applications. Data markets can increase the supply of data, particularly in data-scarce domains such as healthcare,…
Towards Optimal Statistical Watermarking
Baihe Huang, Hanlin Zhu, Banghua Zhu +4
We study statistical watermarking by formulating it as a hypothesis testing problem, a general framework which subsumes all previous statistical watermarking methods. Key to our fo…