17 citations · 32 across the 5 of their papers we have counts for
5 papers
HSRM: Hidden-State Reward Models for Test-Time Verification
Xianzhi Li, Xiaodan Zhu
Large language models can often generate plausible mathematical reasoning traces, but reliably identifying the correct solution among multiple candidates remains a key challenge. E…
Entropy-Gated Branching for Efficient Test-Time Reasoning
Xianzhi Li, Ethan Callanan, Abdellah Ghassel +1
Test-time compute methods can significantly improve the reasoning capabilities and problem-solving accuracy of large language models (LLMs). However, these approaches require subst…
Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
Xianzhi Li, Ran Zmigrod, Zhiqiang Ma +2
Language models are capable of memorizing detailed patterns and information, leading to a double-edged effect: they achieve impressive modeling performance on downstream tasks with…
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou +6
Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art ta…
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
Xianzhi Li, Samuel Chan, Xiaodan Zhu +4
The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide ran…