Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements (2023)

Abstract: 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.

## introduction we introduce vera, a general-purpose commonsense statement verification model. this model is designed to estimate the plausibility of declarative, natural language statements based on commonsense knowledge. we build vera in response to the absence of good detectors of commonsense errors in text generated by language models (lms). lms have been advancing rapidly and have demonstrated remarkable success in various tasks, including question answering, natural language inference, sequence classification, and text generation. yet these models still make simple commonsense mistakes. as shown in figure 1 , as of february 23, 2023, chat-gpt (openai, 2022a) reportedly output the text "since the density of a marble is much less than the density of mercury, the marble would sink to the bottom of the bowl if placed in it", which is clearly incorrect. this kind of failure raises concerns about the reliability and trustworthiness of these models @xcite . vera estimates a plausibility score for a commonsense statement based on its commonsense knowledge about the world. it contrasts with fact verification methods @xcite , which verify the correctness of claims based on evidence from a text corpus. vera enables plausibility estimation where direct evidence is often not retrievable from some corpus, and usually some implicit, fuzzy reasoning is needed. it operates solely with the commonsense knowledge stored in its model parameters, and does not have a retrieval component. vera is built on top of t5 @xcite , a generic pretrained lm, by finetuning on a vast collection of correct and incorrect commonsense statements sourced from knowledge bases (kbs) and question answering (qa) datasets. the 21 data sources @xcite amount to ∼7m statements encompassing a wide spectrum of domains, including general, scientific, physical, and social commonsense, as well as quantitative (reasoning about numbers) and qualitative (reasoning about qualitative relationships such as smaller) commonsense. we propose a novel two-stage training process that takes into account the scale and quality of data from different sources. in addition to the standard multiple-choice binary classification objectives, we adopt a supervised contrastive loss @xcite to magnify the distinction between similar statements with different correctness labels. furthermore, we propose an automatic way of augmenting the training data by eliciting lms to generate incorrect answers to commonsense questions and empirically find it helps generalization. we evaluate vera in the following applications: an example of vera in action is shown in figure 1 . we hope vera can be a useful tool for improving the commonsense correctness of existing generative lm output and inspire more effort toward general-purpose and robust verification methods. ## problem definition and scope our goal is to build a model that can estimate the plausibility of any given commonsense statement. the model takes as input a statement that (1) is expressed in natural language; (2) is declarative, as opposed to interrogative questions; (3) is selfcontained without requiring additional context to comprehend; (4) has an objective, binary correctness label; and (5) in principle can be labeled using widely-held commonsense knowledge about the world. encyclopedic knowledge (e.g., ljubljana is the capital of slovenia.) is out of scope. moving forward, unless explicitly noted, we use commonsense statement to refer to statements within the above scope. though somewhat strict, this scope covers a broad range of potential applications. for an input commonsense statement x, the model should output a real-valued score @xmath0, 1] that represents its estimated plausibility of x. while the gold correctness label is binary, we let the model output a score to reflect its confidence. a score of 1.0 means that it is completely confident that x is correct, and a score of 0.0 means it is completely confident that x is incorrect. when predicting correctness label from the score, we use 0.5 as the threshold. ## method in this section, we describe the whole pipeline to build vera. we start from curating large-scale training data including both correct and incorrect statements from diverse commonsense tasks ( §3.1). next, we learn a scoring model that takes a statement and returns a continuous score by finetuning a lm via 3 training objectives ( §3.2). an additional post hoc calibration strategy is applied to make the output scores well-calibrated ( §3.3). ## experimental setup in this section, we provide more details of model training, the evaluation protocol and metrics, and describe the baseline models we benchmark. ## evaluation results in this section, we evaluate the ability of vera to estimate the plausibility of commonsense statements and compare it with the baseline models. we show the effectiveness of vera in three scenarios: solving commonsense problems, filtering lm-generated commonsense knowledge, and detecting commonsense errors in chatgpt outputs. ## conclusion and future work we introduced vera, a general-purpose verification model for commonsense statements and an early step toward tools for mitigating commonsense errors in text generated by language models. vera achieves state-of-the-art performance when solving commonsense problems in the verification format, excels at filtering lm-generated commonsense knowledge statements, and is found useful in detecting erroneous commonsense statements from generative lms. furthermore, the scores produced by vera are well-calibrated; and could be used for plausibility score estimation for declarative statements if needed. as vera mainly targets on singlesentence statements, future work may consider verification of multi-sentence or long-form statements, or contextualized/defeasible commonsense statements.