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20212026
most citedCan Large Language Models Transform Computational Social Science?

62 citations · 104 across the 24 of their papers we have counts for

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Showing 2023 · cs.CLShow all

6 papers · 2 filters

cs.CL2023★ 4 cited

A Material Lens on Coloniality in NLP

William Held, Camille Harris, Michael Best +1

Coloniality, the continuation of colonial harms beyond "official" colonization, has pervasive effects across society and scientific fields. Natural Language Processing (NLP) is no…

cs.CL2023

Task-Agnostic Low-Rank Adapters for Unseen English Dialects

Zedian Xiao, William Held, Yanchen Liu +1

Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English. As a result, speakers of other dialects experience significant…

cs.CL2023★ 1 cited

Modeling Cross-Cultural Pragmatic Inference with Codenames Duet

Omar Shaikh, Caleb Ziems, William Held +3

Pragmatic reference enables efficient interpersonal communication. Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers an…

cs.CL2023★ 6 cited

TADA: Task-Agnostic Dialect Adapters for English

Will Held, Caleb Ziems, Diyi Yang

Large Language Models, the dominant starting point for Natural Language Processing (NLP) applications, fail at a higher rate for speakers of English dialects other than Standard Am…

cs.CL2023

DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

Yanchen Liu, William Held, Diyi Yang

Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialect…

cs.CL2023★ 62 cited

Can Large Language Models Transform Computational Social Science?

Caleb Ziems, William Held, Omar Shaikh +3

Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify a…