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20232025
most citedA Roadmap to Pluralistic Alignment

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

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5 papers · 1 filter

cs.CL2025★ 2 cited

What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models

Dustin Wright, Sarah Masud, Jared Moore +5

Large language models (LLMs) are increasingly used as primary knowledge sources, yet their epistemic diversity - defined as the diversity of real-world claims in their outputs - ha…

cs.CL2025

AbsenceBench: Language Models Can't Tell What's Missing

Harvey Yiyun Fu, Aryan Shrivastava, Jared Moore +3

Large language models (LLMs) are increasingly capable of processing long inputs and locating specific information within them, as evidenced by their performance on the Needle in a…

cs.CL2024★ 2 cited

Are Large Language Models Consistent over Value-laden Questions?

Jared Moore, Tanvi Deshpande, Diyi Yang

Large language models (LLMs) appear to bias their survey answers toward certain values. Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are th…

cs.CL2024

I am a Strange Dataset: Metalinguistic Tests for Language Models

Tristan Thrush, Jared Moore, Miguel Monares +2

Statements involving metalinguistic self-reference ("This paper has six sections.") are prevalent in many domains. Can current large language models (LLMs) handle such language? In…

cs.CL2023★ 2 cited

LLMs grasp morality in concept

Mark Pock, Andre Ye, Jared Moore

Work in AI ethics and fairness has made much progress in regulating LLMs to reflect certain values, such as fairness, truth, and diversity. However, it has taken the problem of how…