24 citations · 57 across the 26 of their papers we have counts for
5 papers · 1 filter
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
Shangqing Tu, Yuliang Sun, Yushi Bai +3
To mitigate the potential misuse of large language models (LLMs), recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible…
LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts
Shangqing Tu, Zheyuan Zhang, Jifan Yu +5
Teaching assistants have played essential roles in the long history of education. However, few MOOC platforms are providing human or virtual teaching assistants to support learning…
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
Jifan Yu, Xiaozhi Wang, Shangqing Tu +32
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticu…
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
Jifan Yu, Mengying Lu, Qingyang Zhong +9
Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although t…
ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time
Shangqing Tu, Chunyang Li, Jifan Yu +3
ChatGPT has achieved great success and can be considered to have acquired an infrastructural status. There are abundant works for evaluating ChatGPT on benchmarks. However, existin…