Results 221 to 230 of about 289,095 (327)

Language machines: Toward a linguistic anthropology of large language models

Journal of Linguistic Anthropology, Volume 36, Issue 1, May 2026.
Abstract Large language models (LLMs) challenge long‐standing assumptions in linguistics and linguistic anthropology by generating human‐like language without relying on rule‐based structures. This introduction to the special issue Language Machines calls for renewed engagement with LLMs as socially embedded language technologies.
Siri Lamoureaux, Michael Castelle, Anna Weichselbraun   +2 more
wiley   +1 more source

Decoding the bare necessities of decapod crustacean nomenclature through the ages. [PDF]

PeerJ
De Grave S, Cole E, van der Meij SET.
europepmc   +1 more source

Fides et Ratio in the Renaissance [PDF]

, 2012
Petrescu, Lucian
core   +1 more source

The expansion of American mythology in Walter Mosley's Easy Rawlins series

, 2010
W. A. Plummer
openalex   +1 more source

Human tests for machine models: What lies “Beyond the Imitation Game”?

Journal of Linguistic Anthropology, Volume 36, Issue 1, May 2026.
Abstract Benchmarking large language models (LLMs) is a key practice for evaluating their capabilities and risks. This paper considers the development of “BIG Bench,” a crowdsourced benchmark designed to test LLMs “Beyond the Imitation Game.” Drawing on linguistic anthropological and ethnographic analysis of the project's GitHub repository, we examine ...
Noya Kohavi, Anna Weichselbraun
wiley   +1 more source

Combating Global Silicosis Epidemics: Can We Alter Sisyphus's Fate? [PDF]

Am J Respir Crit Care Med
Akgün M.
europepmc   +1 more source

BOOK REVIEW OF THE CLOCKWORK THREE WRITTEN BY MATTHEW J. KIRBY [PDF]

, 2016
Saputra, AndriNovriantoDewa
core  

Co‐textual dopes: How LLMs produce contextually appropriate text in chat interactions with humans without access to context

Journal of Linguistic Anthropology, Volume 36, Issue 1, May 2026.
Abstract This paper asks how LLM‐based systems can produce text that is taken as contextually appropriate by humans without having seen text in its broader context. To understand how this is possible, context and co‐text have to be distinguished. Co‐text is input to LLMs during training and at inference as well as the primary resource of sense‐making ...
Ole Pütz
wiley   +1 more source

Nonhuman situational enmeshments—How participants build temporal infrastructures for ChatGPT

Journal of Linguistic Anthropology, Volume 36, Issue 1, May 2026.
Abstract This paper investigates how participants recruit Large Language Models (LLMs) like ChatGPT as interactional co‐participants depending on their temporal enmeshment within an interactional flow. Using Charles Goodwin's co‐operative action framework, we analyze video data of human–AI interaction to trace the temporal structures established by ...
Nils Klowait, Maria Erofeeva
wiley   +1 more source

Community perceptions towards children living with albinism in Africa: An integrative review. [PDF]

Afr J Disabil
Mokwele T, Mooa RS, Sepeng NV.
europepmc   +1 more source