Results 201 to 210 of about 320,905 (370)

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

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

Disaster Mythology and the Law

, 2011
Lisa Grow Sun
openalex   +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

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

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

On the Mythologies and Ethics of Non-violence: What Do Murray Rothbard and Hugo Chavez Have in Common?

, 2014
N. G. Lawrence
openalex   +1 more source

Dictionary of Classical Mythology

, 2014
J. March
semanticscholar   +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

Budget deficit mythology [PDF]

Preston J. Miller
core