1 citations · 1 across the 6 of their papers we have counts for
6 papers
Behavioral Economics of AI: LLM Biases and Corrections
Pietro Bini, Lin William Cong, Xing Huang +1
Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigat…
P-EAGLE: Parallel-Drafting EAGLE with Scalable Training
Mude Hui, Xin Huang, Jaime Campos Salas +5
Reasoning LLMs produce longer outputs, requiring speculative decoding drafters trained on extended sequences. Parallel drafting - predicting multiple tokens per forward pass - offe…
Reverse Physician-AI Relationship: Full-process Clinical Diagnosis Driven by a Large Language Model
Shicheng Xu, Xin Huang, Zihao Wei +3
Full-process clinical diagnosis in the real world encompasses the entire diagnostic workflow that begins with only an ambiguous chief complaint. While artificial intelligence (AI),…
AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought
Weihua Zheng, Xin Huang, Zhengyuan Liu +7
Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. Although these models show strong reasoning abilities, their pe…
Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning
Wenwen Zhuang, Xin Huang, Xiantao Zhang +1
Multimodal Large Language Models (MLLMs) excel in solving text-based mathematical problems, but they struggle with mathematical diagrams since they are primarily trained on natural…
Preference as Reward, Maximum Preference Optimization with Importance Sampling
Zaifan Jiang, Xing Huang, Chao Wei
Preference learning is a key technology for aligning language models with human values. Reinforcement Learning from Human Feedback (RLHF) is a model-based algorithm to optimize pre…