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Alan Ritter

25 papers hereh-index 9437 citations30 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author16
  • last author8

Across the 24 of 25 papers where every author was matched, so the position is known.

fields
  • cs.CL18
  • cs.CV3
  • cs.HC2
  • cs.CR1
  • cs.LG1
same name
  • Alan Ritter — 37 papers, h 38
  • Alan Ritter — 6 papers, h 4
  • Alan Ritter — 5 papers, h 2
  • Alan Ritter — 4 papers, h 2
  • Alan Ritter — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
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

collaborators
Showing 2024 · cs.CLShow all

4 papers · 2 filters

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.