UAAT International Young Visiting Scholar Program
Kuan-Hao Huang
Current Position/Title: Assistant Professor
Institutional Affiliation : Department of Computer Science and Engineering, Texas A&M University
Email: khhuang@tamu.edu
Webpage: https://khhuang.me/
Host Scholar: (Name and Position)：Professor Hsuan-Tien Lin 林軒田教授
Hosting Department/Institution: Department of Computer Science and Information Engineering
Biography:
Kuan-Hao Huang is an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. Before joining Texas A&M in 2024, he was a Postdoctoral Research Associate at University of Illinois Urbana-Champaign. His research focuses on natural language processing and machine learning, with a particular emphasis on building trustworthy and generalizable language AI systems that can adapt across domains, languages, and modalities. His research has been published in top-tier conferences such as ACL, EMNLP, and ICLR. His work on paraphrase understanding was recognized with the ACL Area Chair Award in 2023.
Lecture [1]:
Time: 14:20 – 15:30, June 4, 2025
Venue: R102, CSIE, NTU
Title: Toward Robust and Reliable Large Language Models
Abstract:
Large language models (LLMs) have shown remarkable potential in real-world applications. Despite their impressive capabilities, they can still produce errors in simple situations and behave in ways that are misaligned with human expectations, raising concerns about their reliability. As a result, ensuring their robustness has become a critical challenge. In this talk, I will explore key robustness issues across three aspects of LLMs: pure text-based LLMs, multimodal LLMs, and multilingual LLMs. Specifically, I will first introduce how position bias can hurt the understanding capabilities of LLMs and present a training-free solution to address this issue. Next, I will discuss position bias in the multimodal setting and introduce a Primal Visual Description (PVD) module that enhances robustness in multimodal understanding. Finally, I will examine the impact of language alignment on the robustness of multilingual LLMs.
Lecture [2]:
Time: 14:00 – 15:00, June 5, 2025
Venue: NTHU Delta Hall A615
Title: Toward Robust and Reliable Large Language Models
Abstract:
Large language models (LLMs) have shown remarkable potential in real-world applications. Despite their impressive capabilities, they can still produce errors in simple situations and behave in ways that are misaligned with human expectations.
- Pure text-based LLMs: Addressing position bias and mitigation
- Multimodal LLMs: Using PVD module to improve vision-language grounding
- Multilingual LLMs: Exploring the effects