7 citations · 9 across the 5 of their papers we have counts for
5 papers
Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs
Jonathan Zheng, Zirui Shao, Alan Ritter +1
Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evaluating knowledge edits either suf…
Probabilistic Reasoning with LLMs for k-anonymity Estimation
Jonathan Zheng, Sauvik Das, Alan Ritter +1
Probabilistic reasoning is a key aspect of both human and artificial intelligence that allows for handling uncertainty and ambiguity in decision-making. In this paper, we introduce…
Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation
Anton Lavrouk, Ian Ligon, Tarek Naous +3
The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and c…
NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms
Jonathan Zheng, Alan Ritter, Wei Xu
The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference. One understudied avenue…
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
Jonathan Zheng, Ashutosh Baheti, Tarek Naous +2
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the lar…