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Imprinting: Knowledge Distillation Can Shift Student Behavior
Yu-Chien Tang, An-Zi Yen
Knowledge distillation trains student models on teacher-generated responses to transfer task capability, but those responses may also carry behavioral tendencies of their source. W…
EgoArgus: Benchmarking VLMs as Situational Assistants for Modality-Grounded User Supports
Yu-Chien Tang, Yu-Hsiang Liu, An-Zi Yen
VLMs are increasingly positioned as daily assistants that perceive first-person environments, follow user dialogue, and decide how to help. Existing egocentric benchmarks mainly ev…
VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval
Yu-Chien Tang, Jun-Chen Hung, Wen-Chih Peng +1
Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routing has emerged as a practical so…
VISTA: A Controllable Platform for Generating and Auditing Egocentric Assistance Scenarios
Yu-Hsiang Liu, Yu-Chien Tang, An-Zi Yen
Evaluating whether AI agents can proactively assist humans in daily activities, ranging from routine household tasks to urgent safety-critical situations, requires diverse visual d…
Tree-of-Text: A Tree-based Prompting Framework for Table-to-Text Generation in the Sports Domain
Shang-Hsuan Chiang, Tsan-Tsung Yang, An-Zi Yen +1
Generating sports game reports from structured tables is a complex table-to-text task that demands both precise data interpretation and fluent narrative generation. Traditional mod…
ConceptKT: A Benchmark for Concept-Level Deficiency Prediction in Knowledge Tracing
Yu-Chen Kang, Yu-Chien Tang, An-Zi Yen
Knowledge Tracing (KT) is a critical technique for modeling student knowledge to support personalized learning. However, most KT systems focus on binary correctness prediction and…
OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference
Yow-Fu Liou, Yu-Chien Tang, Yu-Hsiang Liu +1
Benchmarking large language models (LLMs) is critical for understanding their capabilities, limitations, and robustness. In addition to interface artifacts, prior studies have show…
CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models
Yong-En Tian, Yu-Chien Tang, An-Zi Yen +1
Aspect-based summarization has attracted significant attention for its ability to generate more fine-grained and user-aligned summaries. While most existing approaches assume a set…
MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support
Wei-Ling Hsu, Yu-Chien Tang, An-Zi Yen
The increasing reliance on Large Language Models (LLMs) across various domains extends to education, where students progressively use generative AI as a tool for learning. While pr…
Stay Hungry, Stay Foolish: On the Extended Reading Articles Generation with LLMs
Yow-Fu Liou, Yu-Chien Tang, An-Zi Yen
The process of creating educational materials is both time-consuming and demanding for educators. This research explores the potential of Large Language Models (LLMs) to streamline…
Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning
An-Zi Yen, Wei-Ling Hsu
Due to the remarkable language understanding and generation abilities of large language models (LLMs), their use in educational applications has been explored. However, little work…
RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training
Yu-Chien Tang, Wei-Yao Wang, An-Zi Yen +1
The dialogue systems in customer services have been developed with neural models to provide users with precise answers and round-the-clock support in task-oriented conversations by…