1 citations · 1 across the 12 of their papers we have counts for
12 papers
AIA: Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness
Hanshu Rao, Guangzeng Han, Xiaolei Huang
Attributes describing data content and context can induce diverse imbalance patterns that go beyond label imbalance alone. However, existing studies primarily address label imbalan…
Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction
Guangzeng Han, James G. Murphy, Benjamin O. Ladd +2
BACKGROUND: Coding Motivational Interviewing (MI) sessions is essential for understanding client behaviors and predicting outcomes, but it requires substantial time and labor from…
What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective
Guangzeng Han, Xiaolei Huang
Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an instruction data selection f…
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
Weisi Liu, Guangzeng Han, Xiaolei Huang
Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data where semantic distributions…
Model-Agnostic Meta Learning for Class Imbalance Adaptation
Hanshu Rao, Guangzeng Han, Xiaolei Huang
Class imbalance is a widespread challenge in NLP tasks, significantly hindering robust performance across diverse domains and applications. We introduce Hardness-Aware Meta-Resampl…
From Scientific Crop Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping
Yu Wu, Guangzeng Han, Ibra Niang Niang +6
High-throughput, effective and comprehensive phenotyping is a critical prerequisite for crop improvement. While phenotypic analysis was traditionally performed manually, recent adv…
Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Guangzeng Han, Weisi Liu, Xiaolei Huang
Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combi…
Cultivating Multidisciplinary AI Workforce Development on iTiger GPU Cluster: Practices and Challenges
Mayira Sharif, Guangzeng Han, Weisi Liu +1
To support rapid AI advances and broaden access to large-scale computing resources for under-resourced institutions at the Mid-South, we established the first regional mid-scale GP…
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts
Weisi Liu, Guangzeng Han, Xiaolei Huang
Time is implicitly embedded in classification process: classifiers are usually built on existing data while to be applied on future data whose distributions (e.g., label and token)…
Can MLLMs Understand the Deep Implication Behind Chinese Images?
Chenhao Zhang, Xi Feng, Yuelin Bai +18
As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…
Length-Aware Multi-Kernel Transformer for Long Document Classification
Guangzeng Han, Jack Tsao, Xiaolei Huang
Lengthy documents pose a unique challenge to neural language models due to substantial memory consumption. While existing state-of-the-art (SOTA) models segment long texts into equ…
Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts
Guangzeng Han, Weisi Liu, Xiaolei Huang +1
Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication interventio…