24 citations · 100 across the 44 of their papers we have counts for
13 papers · 1 filter
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination
Daniel Zhang-Li, Zheyuan Zhang, Jifan Yu +8
The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to…
From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
Jifan Yu, Zheyuan Zhang, Daniel Zhang-li +30
Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to r…
R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models
Shangqing Tu, Yuanchun Wang, Jifan Yu +6
Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Recently, various Retrieval-Augmented Large Langu…
Simulating Classroom Education with LLM-Empowered Agents
Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu +9
Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching. While preliminary studies have focused on task-specific, independent…
Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language Models
Shangqing Tu, Zhuoran Pan, Wenxuan Wang +6
Large language models (LLMs) have been increasingly applied to various domains, which triggers increasing concerns about LLMs' safety on specialized domains, e.g. medicine. Despite…
Explainable Few-shot Knowledge Tracing
Haoxuan Li, Jifan Yu, Yuanxin Ouyang +4
Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educat…
SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking
Yuanchun Wang, Jifan Yu, Zijun Yao +13
Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts. However, current LLM API-using methods str…
Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking
Xiaokang Zhang, Zijun Yao, Jing Zhang +4
Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations. Current factuality probes, trained using humananno…
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models
Yantao Liu, Zijun Yao, Xin Lv +5
Providing knowledge documents for large language models (LLMs) has emerged as a promising solution to update the static knowledge inherent in their parameters. However, knowledge i…
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction
Yuchen Fan, Yantao Liu, Zijun Yao +3
Modern Large Language Models (LLMs) have showcased remarkable prowess in various tasks necessitating sophisticated cognitive behaviors. Nevertheless, a paradoxical performance disc…
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
Jifan Yu, Xiaohan Zhang, Yifan Xu +5
Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. Ho…
Reverse That Number! Decoding Order Matters in Arithmetic Learning
Daniel Zhang-Li, Nianyi Lin, Jifan Yu +6
Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite ac…
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
Xiaokang Zhang, Sijia Luo, Bohan Zhang +12
We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embe…