6 citations · 7 across the 8 of their papers we have counts for
8 papers
Test-Driven, AI-Assisted Learning: Replacing Lectures with Weekly Closed-Book Tests
Jin-Guo Liu, Shang-Qi Lu, Xin-Ran Shi +2
This paper is an experience report on a 13-week Test-Driven, AI-Assisted (TDAA) redesign of DSAA 3071, Theory of Computation, an upper-level course at the Hong Kong University of S…
Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
Liuji Chen, Dianxing Tang, Xing Shi +4
Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing approaches mitigate this issue w…
SSPO: Self-traced Step-wise Preference Optimization for Process Supervision and Reasoning Compression
Yuyang Xu, Yi Cheng, Haochao Ying +5
Test-time scaling has proven effective in further enhancing the performance of pretrained Large Language Models (LLMs). However, mainstream post-training methods (i.e., reinforceme…
Large Language Models Could Be Rote Learners
Yuyang Xu, Renjun Hu, Haochao Ying +3
Benchmark-based evaluation, e.g., multiple-choice questions (MCQs) and open-ended questions (OEQs), is widely used for evaluating Large Language Models (LLMs), yet their reliabilit…
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons
Renjun Hu, Yi Cheng, Libin Meng +4
The rapid advancement of large language models (LLMs) has opened new possibilities for their adoption as evaluative judges. This paper introduces Themis, a fine-tuned LLM judge tha…
Behavior Modeling Space Reconstruction for E-Commerce Search
Yejing Wang, Chi Zhang, Xiangyu Zhao +8
Delivering superior search services is crucial for enhancing customer experience and driving revenue growth. Conventionally, search systems model user behaviors by combining user p…
PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations
Jiatong Li, Renjun Hu, Kunzhe Huang +5
Expert-designed close-ended benchmarks are indispensable in assessing the knowledge capacity of large language models (LLMs). Despite their widespread use, concerns have mounted re…
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
Yi Cheng, Renjun Hu, Haochao Ying +3
Until recently, the question of the effective inductive bias of deep models on tabular data has remained unanswered. This paper investigates the hypothesis that arithmetic feature…