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20232026
most citedNEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms

7 citations · 18 across the 24 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.HC2024

Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI

Isadora Krsek, Anubha Kabra, Yao Dou +5

In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024★ 7 cited

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…