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20152026
most citedDemographic Inference and Representative Population Estimates from Multilingual Social Media Data

177 citations · 475 across the 40 of their papers we have counts for

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Showing 2024 · cs.CLShow all

6 papers · 2 filters

cs.CL2024

HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter

Manuel Tonneau, Diyi Liu, Niyati Malhotra +4

To address the global challenge of online hate speech, prior research has developed detection models to flag such content on social media. However, due to systematic biases in eval…

cs.CL2024★ 3 cited

Evidence of a log scaling law for political persuasion with large language models

Kobi Hackenburg, Ben M. Tappin, Paul Röttger +3

Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with…

cs.CL2024★ 1 cited

LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages

Andrew M. Bean, Simi Hellsten, Harry Mayne +5

In this paper, we present the LingOly benchmark, a novel benchmark for advanced reasoning abilities in large language models. Using challenging Linguistic Olympiad puzzles, we eval…

cs.CL2024★ 5 cited

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…

cs.CL2024

From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets

Manuel Tonneau, Diyi Liu, Samuel Fraiberger +3

Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural bias…

cs.CL2024★ 5 cited

The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

Hannah Rose Kirk, Alexander Whitefield, Paul Röttger +9

Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to wha…