11 citations · 23 across the 15 of their papers we have counts for
15 papers
What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search
Xinhao Zhang, Xi Chen, François Portet +1
Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these o…
From Imitation to Discrimination: Progressive Curriculum Learning for Robust Web Navigation
Chuang Peng, Wei Zhang, Renshuai Tao +2
Text-based web agents offer computational efficiency for autonomous web navigation, yet developing robust agents remains challenging due to the noisy and heterogeneous nature of re…
Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference
Yifu Ding, Xinhao Zhang, Jinyang Guo
Transformer-based large language models (LLMs) have demonstrated remarkable performance across a wide range of real-world tasks, but their inference cost remains prohibitively high…
RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
Zui Chen, Han Li, Xinhao Zhang +12
We present RubikSQL, a novel NL2SQL system designed to address key challenges in real-world enterprise-level NL2SQL, such as implicit intents and domain-specific terminology. Rubik…
SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM
Pengjiang Li, Zaitian Wang, Xinhao Zhang +4
Topic discovery in scientific literature provides valuable insights for researchers to identify emerging trends and explore new avenues for investigation, facilitating easier scien…
Distilling Empathy from Large Language Models
Henry J. Xie, Jinghan Zhang, Xinhao Zhang +1
The distillation of knowledge from Large Language Models (LLMs) into Smaller Language Models (SLMs), preserving the capabilities and performance of LLMs while reducing model size,…
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…
LEKA:LLM-Enhanced Knowledge Augmentation
Xinhao Zhang, Jinghan Zhang, Fengran Mo +3
Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's per…
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…
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…
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Xinhao Zhang, Jinghan Zhang, Fengran Mo +4
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is to expand the current fe…
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…
TFWT: Tabular Feature Weighting with Transformer
Xinhao Zhang, Zaitian Wang, Lu Jiang +3
In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume…