7 citations · 12 across the 14 of their papers we have counts for
9 papers · 1 filter
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Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time.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…
Investigating and Alleviating Harm Amplification in LLM Interactions
Ruohao Guo, Wei Xu, Alan Ritter
Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their…
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…
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
Ruohao Guo, Wei Xu, Alan Ritter
As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current…
Granular Privacy Control for Geolocation with Vision Language Models
Ethan Mendes, Yang Chen, James Hays +3
Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. As these models are widely deployed in consumer applications, they…
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…
Constrained Decoding for Cross-lingual Label Projection
Duong Minh Le, Yang Chen, Alan Ritter +1
Zero-shot cross-lingual transfer utilizing multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data. However, for NLP tasks…
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…
Can Language Models be Instructed to Protect Personal Information?
Yang Chen, Ethan Mendes, Sauvik Das +2
Large multimodal language models have proven transformative in numerous applications. However, these models have been shown to memorize and leak pre-training data, raising serious…