5 citations · 6 across the 4 of their papers we have counts for
5 papers
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…
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…
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…