# If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models

arXiv: https://arxiv.org/abs/2510.08388 · HTML: https://mlsanity.com/paper/2510.08388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [Transformers as Soft Reasoners over Language](https://mlsanity.com/paper/2002.05867/) (2020)
- [Large Language Models are Inconsistent and Biased Evaluators](https://mlsanity.com/paper/2405.01724/) (2024)
- [What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering](https://mlsanity.com/paper/2406.12334/) (2024)
- [On Verbalized Confidence Scores for LLMs](https://mlsanity.com/paper/2412.14737/) (2024)
- [The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code](https://mlsanity.com/paper/2305.19213/) (2023)
- [Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning](https://mlsanity.com/paper/2402.14856/) (2024)
- [Do Large Language Models Reason Causally Like Us? Even Better?](https://mlsanity.com/paper/2502.10215/) (2025)

## Abstract (arXiv)

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 textitconditional probability of B given A, and the textitsemantic 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 textitacceptability 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 relevanceunicodex2014though 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.