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arXiv · 2502.13464

Estimating Commonsense Plausibility through Semantic Shifts

Wanqing Cui·Wei Huang·Keping Bi·Jiafeng Guo·Xueqi Cheng
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Abstract

Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or verbalized judgments--struggle with fine-grained discrimination. In this paper, we propose ComPaSS, a novel discriminative framework that quantifies commonsense plausibility by measuring semantic shifts when augmenting sentences with commonsense-related information. Plausible augmentations induce minimal shifts in semantics, while implausible ones result in substantial deviations. Evaluations on two types of fine-grained commonsense plausibility estimation tasks across different backbones, including LLMs and vision-language models (VLMs), show that ComPaSS consistently outperforms baselines. It demonstrates the advantage of discriminative approaches over generative methods in fine-grained commonsense plausibility evaluation. Experiments also show that (1) VLMs yield superior performance to LMs, when integrated with ComPaSS, on vision-grounded commonsense tasks. (2) contrastive pre-training sharpens backbone models' ability to capture semantic nuances, thereby further enhancing ComPaSS.

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Wanqing Cui, Wei Huang, Keping Bi, Jiafeng Guo, Xueqi Cheng. 2026-04-20. Estimating Commonsense Plausibility through Semantic Shifts. https://arxiv.org/abs/2502.13464

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