1 citations · 2 across the 5 of their papers we have counts for
6 papers
Controllable Concept Bottleneck Models
Hongbin Lin, Chenyang Ren, Juangui Xu +7
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most prev…
Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao +5
Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting t…
LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System
Tianfu Wang, Yi Zhan, Jianxun Lian +5
Intelligent Tutoring Systems (ITSs) have revolutionized education by offering personalized learning experiences. However, as goal-oriented learning, which emphasizes efficiently ac…
Towards Acyclic Preference Evaluation of Language Models via Multiple Evaluators
Zhengyu Hu, Jieyu Zhang, Zhihan Xiong +3
Despite the remarkable success of Large Language Models (LLMs), evaluating their outputs' quality regarding preference remains a critical challenge. While existing works usually le…
Explaining Length Bias in LLM-Based Preference Evaluations
Zhengyu Hu, Linxin Song, Jieyu Zhang +7
The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermin…
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking
Zhengyu Hu, Jieyu Zhang, Yue Yu +2
This paper presents LEMR (Label-Efficient Model Ranking) and introduces the MoraBench Benchmark. LEMR is a novel framework that minimizes the need for costly annotations in model s…