11 citations · 25 across the 8 of their papers we have counts for
8 papers
Diversity-oriented Data Augmentation with Large Language Models
Zaitian Wang, Jinghan Zhang, Xinhao Zhang +3
Data augmentation is an essential technique in natural language processing (NLP) for enriching training datasets by generating diverse samples. This process is crucial for improvin…
Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues
Henry J. Xie, Jinghan Zhang, Xinhao Zhang +1
In recent years, Large Language Models (LLMs) have become increasingly more powerful in their ability to complete complex tasks. One such task in which LLMs are often employed is s…
Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation
Xinhao Zhang, Jinghan Zhang, Wujun Si +1
Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks. However, its efficacy is often limited by static training modes and…
Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
Jinghan Zhang, Henry Xie, Xinhao Zhang +1
In the financial field, precise risk assessment tools are essential for decision-making. Recent studies have challenged the notion that traditional network loss functions like Mean…
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +4
Large language models have shown strong reasoning capabilities through chain-structured methods such as Chain-of-Thought. Recent studies optimize thought structures by generating p…
Prototypical Reward Network for Data-Efficient RLHF
Jinghan Zhang, Xiting Wang, Yiqiao Jin +3
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF…
Dynamic and Adaptive Feature Generation with LLM
Xinhao Zhang, Jinghan Zhang, Banafsheh Rekabdar +3
The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algor…
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
Jinghan Zhang, Xiting Wang, Weijieying Ren +3
Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Au…