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commonsense plausibility

Commonsense plausibility is the degree to which an assertion, event, or scenario is believable and consistent with shared, everyday human understanding of how the world functions. Unlike formal logical validity or strict factual correctness, which depend on rigorous deduction or specialized domain knowledge, commonsense plausibility evaluates whether a claim conforms to intuitive expectations about physical properties, cause and effect, spatial and temporal relationships, and standard social dynamics. In artificial intelligence and computational linguistics, assessing this quality provides an estimate of how well declarative statements align with the fundamental, often unstated assumptions of ordinary human experience.

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Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements

Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements

Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi

OrganizationsAllen Institute for AIHarvard UniversityNanyang Technological UniversityUniversity of Washington

Why you should read this

Presents VERA, a standalone commonsense verification model trained on millions of statements that outperforms systems like GPT-4 in estimating the plausibility of declarative claims and detecting errors in language model outputs.

Today’s language models can be remarkably intelligent yet still produce text that contains trivial commonsense errors. Therefore, we seek a retrospective verification approach that can reflect on the commonsense plausibility of the machine text, and introduce VERA, a general-purpose model that learns to estimate the commonsense plausibility of declarative statements. To support diverse commonsense domains, VERA is trained on ~7M commonsense statements that are automatically converted from 19 QA datasets and two commonsense knowledge bases, and using a combination of three training objectives. When applied to solving commonsense problems in the verification format, VERA substantially outperforms existing models that can be repurposed for commonsense verification, even including GPT-3.5/ChatGPT/GPT-4, and it further exhibits generalization capabilities to unseen tasks and provides well-calibrated outputs. We find that VERA excels at filtering machine-generated commonsense knowledge and is useful in detecting erroneous commonsense statements generated by models like ChatGPT in real-world settings.

Added

2026-09-26