@inproceedings{brubaker-etal-2026-wugnectives,
title = "Wugnectives: Novel Entity Inferences of Language Models from Discourse Connectives",
author = "Brubaker, Daniel and
Sheffield, William and
Li, Junyi Jessy and
Misra, Kanishka",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.289/",
pages = "6109--6127",
ISBN = "979-8-89176-380-7",
abstract = "The role of world knowledge has been particularly crucial to predict the discourse connective that marks the discourse relation between two arguments, with language models (LMs) being generally successful at this task. We flip this premise in our work, and instead study the inverse problem of understanding whether discourse connectives can inform LMs about the world. To this end, we present Wugnectives, a dataset of 8,880 stimuli that evaluates LMs' inferences about novel entities in contexts where connectives link the entities to particular attributes. On investigating 17 different LMs at various scales, and training regimens, we found that tuning an LM to show reasoning behavior yields noteworthy improvements on most connectives. At the same time, there was a large variation in LMs' overall performance across connective type, with all models systematically struggling on connectives that express a concessive meaning. Our findings pave the way for more nuanced investigations into the functional role of language cues as captured by LMs.We release Wugnectives at https://github.com/kanishkamisra/wugnectives"
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<abstract>The role of world knowledge has been particularly crucial to predict the discourse connective that marks the discourse relation between two arguments, with language models (LMs) being generally successful at this task. We flip this premise in our work, and instead study the inverse problem of understanding whether discourse connectives can inform LMs about the world. To this end, we present Wugnectives, a dataset of 8,880 stimuli that evaluates LMs’ inferences about novel entities in contexts where connectives link the entities to particular attributes. On investigating 17 different LMs at various scales, and training regimens, we found that tuning an LM to show reasoning behavior yields noteworthy improvements on most connectives. At the same time, there was a large variation in LMs’ overall performance across connective type, with all models systematically struggling on connectives that express a concessive meaning. Our findings pave the way for more nuanced investigations into the functional role of language cues as captured by LMs.We release Wugnectives at https://github.com/kanishkamisra/wugnectives</abstract>
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%0 Conference Proceedings
%T Wugnectives: Novel Entity Inferences of Language Models from Discourse Connectives
%A Brubaker, Daniel
%A Sheffield, William
%A Li, Junyi Jessy
%A Misra, Kanishka
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F brubaker-etal-2026-wugnectives
%X The role of world knowledge has been particularly crucial to predict the discourse connective that marks the discourse relation between two arguments, with language models (LMs) being generally successful at this task. We flip this premise in our work, and instead study the inverse problem of understanding whether discourse connectives can inform LMs about the world. To this end, we present Wugnectives, a dataset of 8,880 stimuli that evaluates LMs’ inferences about novel entities in contexts where connectives link the entities to particular attributes. On investigating 17 different LMs at various scales, and training regimens, we found that tuning an LM to show reasoning behavior yields noteworthy improvements on most connectives. At the same time, there was a large variation in LMs’ overall performance across connective type, with all models systematically struggling on connectives that express a concessive meaning. Our findings pave the way for more nuanced investigations into the functional role of language cues as captured by LMs.We release Wugnectives at https://github.com/kanishkamisra/wugnectives
%U https://aclanthology.org/2026.eacl-long.289/
%P 6109-6127
Markdown (Informal)
[Wugnectives: Novel Entity Inferences of Language Models from Discourse Connectives](https://aclanthology.org/2026.eacl-long.289/) (Brubaker et al., EACL 2026)
ACL