@inproceedings{orth-etal-2026-probable,
title = "If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models",
author = "Orth, Jasmin and
Mondorf, Philipp and
Plank, Barbara",
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.18/",
pages = "405--427",
ISBN = "979-8-89176-380-7",
abstract = "Conditional acceptability refers to how plausible a conditional statement is perceived to be. It plays an important role in communication and reasoning, as it influences how individuals interpret implications, assess arguments, and make decisions based on hypothetical scenarios. When humans evaluate how acceptable a conditional ``If A, then B'' is, their judgments are influenced by two main factors: the $\textit{conditional probability}$ of $B$ given $A$, and the $\textit{semantic relevance}$ of the antecedent $A$ given the consequent $B$ (i.e., whether $A$ meaningfully supports $B$). While prior work has examined how large language models (LLMs) draw inferences about conditional statements, it remains unclear how these models judge the $\textit{acceptability}$ of such statements. To address this gap, we present a comprehensive study of LLMs' conditional acceptability judgments across different model families, sizes, and prompting strategies. Using linear mixed-effects models and ANOVA tests, we find that models are sensitive to both conditional probability and semantic relevance$\textemdash{}$though to varying degrees depending on architecture and prompting style. A comparison with human data reveals that while LLMs incorporate probabilistic and semantic cues, they do so less consistently than humans. Notably, larger models do not necessarily align more closely with human judgments."
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<abstract>Conditional acceptability refers to how plausible a conditional statement is perceived to be. It plays an important role in communication and reasoning, as it influences how individuals interpret implications, assess arguments, and make decisions based on hypothetical scenarios. When humans evaluate how acceptable a conditional “If A, then B” is, their judgments are influenced by two main factors: the conditional probability of B given A, and the semantic relevance of the antecedent A given the consequent B (i.e., whether A meaningfully supports B). While prior work has examined how large language models (LLMs) draw inferences about conditional statements, it remains unclear how these models judge the acceptability of such statements. To address this gap, we present a comprehensive study of LLMs’ conditional acceptability judgments across different model families, sizes, and prompting strategies. Using linear mixed-effects models and ANOVA tests, we find that models are sensitive to both conditional probability and semantic relevance—though to varying degrees depending on architecture and prompting style. A comparison with human data reveals that while LLMs incorporate probabilistic and semantic cues, they do so less consistently than humans. Notably, larger models do not necessarily align more closely with human judgments.</abstract>
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%0 Conference Proceedings
%T If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models
%A Orth, Jasmin
%A Mondorf, Philipp
%A Plank, Barbara
%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 orth-etal-2026-probable
%X Conditional acceptability refers to how plausible a conditional statement is perceived to be. It plays an important role in communication and reasoning, as it influences how individuals interpret implications, assess arguments, and make decisions based on hypothetical scenarios. When humans evaluate how acceptable a conditional “If A, then B” is, their judgments are influenced by two main factors: the conditional probability of B given A, and the semantic relevance of the antecedent A given the consequent B (i.e., whether A meaningfully supports B). While prior work has examined how large language models (LLMs) draw inferences about conditional statements, it remains unclear how these models judge the acceptability of such statements. To address this gap, we present a comprehensive study of LLMs’ conditional acceptability judgments across different model families, sizes, and prompting strategies. Using linear mixed-effects models and ANOVA tests, we find that models are sensitive to both conditional probability and semantic relevance—though to varying degrees depending on architecture and prompting style. A comparison with human data reveals that while LLMs incorporate probabilistic and semantic cues, they do so less consistently than humans. Notably, larger models do not necessarily align more closely with human judgments.
%U https://aclanthology.org/2026.eacl-long.18/
%P 405-427
Markdown (Informal)
[If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models](https://aclanthology.org/2026.eacl-long.18/) (Orth et al., EACL 2026)
ACL