activity
20142026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 1.2k across the 48 of their papers we have counts for

collaborators
Showing 2023 · cs.CLShow all

9 papers · 2 filters

cs.CL2023★ 1 cited

When it Rains, it Pours: Modeling Media Storms and the News Ecosystem

Benjamin Litterer, David Jurgens, Dallas Card

Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasti…

cs.CL2023★ 2 cited

Social Meme-ing: Measuring Linguistic Variation in Memes

Naitian Zhou, David Jurgens, David Bamman

Much work in the space of NLP has used computational methods to explore sociolinguistic variation in text. In this paper, we argue that memes, as multimodal forms of language compr…

cs.CL2023★ 14 cited

When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models

Mingqian Zheng, Jiaxin Pei, Lajanugen Logeswaran +2

Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGP…

cs.CL2023★ 3 cited

Sociodemographic Prompting is Not Yet an Effective Approach for Simulating Subjective Judgments with LLMs

Huaman Sun, Jiaxin Pei, Minje Choi +1

Human judgments are inherently subjective and are actively affected by personal traits such as gender and ethnicity. While Large Language Models (LLMs) are widely used to simulate…

cs.CL2023★ 1 cited

You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments

Bangzhao Shu, Lechen Zhang, Minje Choi +5

The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. To properly understand the properties…

cs.CL2023

Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics

Aparna Ananthasubramaniam, Hong Chen, Jason Yan +7

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of o…

cs.CL2023

Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships

David Jurgens, Agrima Seth, Jackson Sargent +2

Understanding interpersonal communication requires, in part, understanding the social context and norms in which a message is said. However, current methods for identifying offensi…

cs.CL2023★ 1 cited

When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset

Jiaxin Pei, David Jurgens

Annotators are not fungible. Their demographics, life experiences, and backgrounds all contribute to how they label data. However, NLP has only recently considered how annotator id…

cs.CL2023★ 36 cited

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

Minje Choi, Jiaxin Pei, Sagar Kumar +2

Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks. While LLMs are increasingly deployed in many forms including…