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commonsense verification

Commonsense verification is the computational task of assessing whether a statement or assertion is plausible according to everyday, real-world human understanding. Unlike formal fact-checking that references specialized databases or academic records, commonsense verification focuses on judging the likelihood and validity of ordinary physical, social, and practical scenarios that people intuitively comprehend. In artificial intelligence and natural language processing, this process typically involves estimating the plausibility of declarative claims to detect logical absurdities, filter flawed machine-generated text, and ensure that automated reasoning aligns with shared human knowledge.

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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