Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Authors: Jiacheng Liu
Wenya Wang
Dianzhuo Wang
Noah A. Smith
Yejin Choi
Hannaneh Hajishirzi
Venue: EMNLP 2023 Main (2023)
Keywords: commonsense
verification
plausibility

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 $\sim$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.

VERA: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Jiacheng Liu 2x Wenya Wang &
Dianzhuo Wang
Noah A. Smith Yejin Choi Hannaneh Hajishirzi"
Paul G. Allen School of Computer Science & Engineering, University of Washington
Allen Institute for Artificial Intelligence Nanyang Technological University
John A. Paulson School of Engineering and Applied Sciences, Harvard University
liujcêcs, washington edu
"Equal contribution.
Abstract
a mercurr??
Today's language models can be remarkably intelligent yet still produce text that contains trivial commonsense errors. Therefore, seek a retrospective verification approach that can reflect on the commonsense plausibility of the machine text, and introduce VERA, general-purpose model that learns to estimate the commonsense plausibility of declarative statements. To support diverse commonsense domains, VERA is trained on ~7M common- statements that are automatically con- verted from 19 QA datasets and two common- sense knowledge bases, and using a combi- nation of three training objectives. When ap- plied to solving commonsense problems in the verification format, VERA substantially out- performs existing models that can be repur- posed for commonsense verification, even in- and fur- ther exhibits generalization capabilities to un- seen tasks and provides well-calibrated outputs. We find that VERA excels filtering machine- generated commonsense knowledge and is use- ful detecting erroneous commonsense state- ments generated by models like ChatGPT in real-world settings.
the denelty of marble much than the denalty mereury, the marble would sink to tae botson of the bow) it plased in it.
Chandan
Incorrecti (score 0.04)
Vera
Biuse the decsity of marble mushh loss than the denally of meroury, the marble would float placed mereury.
Orasle
Oorrect (score 0.98)
Figure I: VERA estimates the correctness of declarative statements. Example adapted from a contribution made by Henry Minsky to Mareus and Davis (2023) Febru- ary 23, 2023.
"since the density of marble is much less than the density of mercury, the marble would sink 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 (Lin et al., 2022).
cluding
VERA estimates plausibility score for a com- monsense statement based on its commonsense knowledge about the world. It contrasts with fith verification methods (Thorne et al., 2018; Wadden et al., 2020), which verify the correctness of claims based on evidence from a text corpus. VERA en- ables plausibility estimation where direct evidence is often not retrievable from some corpus, and usu- ally some implicit, fuzzy reasoning is needed. It operates solely with the commonsense knowledge stored in its model parameters, and does not have retrieval component.
1 Introduction
We introduce VERA, general-purpost common- sense statement verification model. This model is designed to estimate the plausibility of declar- ative, natural language statements based on com- monsense knowledge.
We build VERA in response to the absence of good detectors of commonsense errors in text gen- erated by language models (LMs). LMs have been advancing rapidly and have demonstrated remark- able success in various tasks, including question answering, natural language inference, sequence classification, and text generation. Yet these mod- els still make simple commonsense mistakes. As shown in Figure I, as of February 23, 2023, Chat- GPT (OpenAI, 2022a) reportedly output the text
VERA is built on top of T5 (Raffel et al., 2020), 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 (Table 5, appendix) amount to ~MM state- ments encompassing a wide spectrum of domains, including general, scientific, physical, and social commonsense, as well as quantitative (reasoning about numbers) and qualitative (reasoning about

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qualitative relationships such as smaller) common- sense. We propose novel two-stage training pro- cess that takes into account the scale and quality of data from different sources. In addition to the standard multiple-choice binary classification ob- jectives, we adopt supervised contrastive loss (Khosla et al., 2020) to magnify the distinction be- tween similar statements with different correctness labels. Furthermore, propose an automatic way of augmenting the training data by eliciting LMs to generate incorrect answers to commonsense ques- tions and empirically find it helps generalization. We evaluate VERA in the following applications:
as opposed to interrogative questions; (3) is self- contained without requiring additional context comprehend: (4) has an objective, binary correct- ness label; and (5) in principle can be labeled using widely-held commonsense knowledge about the world. Encyclopedic knowledge (c.g., Ljubljana is the capital of Slovenia.) is out of scope. Moving forward, unless explicitly noted, we use common- sense statement refer statements within the above scope. Though somewhat strict, this scope covers broad range of potential applications.
For an input commonsense statement æ, the model should output real-valued score E [o, 1] that represents its estimated plausibility of æ. While the gold correctness label is binary, we let the model output 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 is completely confident that is incorrect. When pre- dicting correctness label from the score, we use 0.5 as the threshold.
Excelling in commonsense problems over GPT-series when repurposed for verifica- tion ($5.1). VERA can be applied to solve multiple-choice and boolean commonsense problems when expressed in the verification format, by scoring and ranking candidate hy- potheses. It substantially outperforms existing models repurposed for commonsense verifica- tion (including GPT-3.5, ChatGPT and GPT- 4), improving upon the best existing baseline, Flan-T5, with absolute improvement of 6% on seen benchmarks and 4% on unseen ones. Filtering LM-generated commonsense knowledge ($5.2). VERA can filter noisy com- monsense knowledge statements generated by other LMs, improving the effectiveness of L.Mggeneraed knowledge in downstream knowledge-augmentte inferences. VERA is well-calibrated, enabling filtering at customized thresholds.
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 state- ment 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).
3.1 Data Construction
Detecting commonsense errors in ChatGPT outputs ($5.3). Through a preliminary analy- sis, we find that VERA can identify common- sense errors made by ChatGPT mn-hhe-wild, with a precision 91% and a recall of 74%. An example of VERA in action is shown in Figure 1.
Labeled commonsense statements usually do not appear in text in the wild, while some common- sense question answering (QA) datasets and com- monsense knowledge bases (KBs) are good sources for this kind of statements. We collect correct and incorrect commonsense statements from the above two types of data source. Table shows some ex- amples on how these statements can be converted from QA problems and KB entries. In total, we obtain ~7M statements (for training) from 19 QA datasets ($3.1.1) and two KBs ($3.1.2) that encom- pass wide spectrum of commonsense domains. Table (appendix) lists these datasets with statis- tics. All datasets we use are publicly available.
We hope VERA can be useful tool for improv- ing the commonsense correctness of existing gen- erative LM output and inspire more effort toward general-pune purposs and robust verification methods.
2 Problem Definition and Scope
Our goal is 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,
3.1.1 From Commonsense QA Datasets
Numerous commonsense reasoning datasets have been published in recent years (Davis, 2023), and

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Original example:
the choice is continuation to it, concate- nate the question and the choice.
KXOW NOW.EDEE.AA BATE BOOLEAN Q4 MULTIPEE-HHOIE Q+
Vhat Nould soneone wear to protect thenselves from canmon?
ungulate borber (C) body tark hat
AnSwer: (C)
Converted statement group:
If there is no question and the problem only asks to choose between some choices, we use the choice as the declarative statement.
world aear boster protect theeanlvss fros cammon. One
ungulate
thenselves fros
one would wear body amer to pretect thenselves free cannon.
thexselves fron One world aear hat protect thessel vas from carnon.
One would
Original example:
For boolean problems, we always use yes as the choice and create single declarative state- ment for each problem. We use the original label as the correctness label of this statement.
Can
follds Instruction nanual?
:nswer::
Converted Convertedstatemmn statemunt group
follow instruction nanual.
Original example
Rublier stamps provide a way to sike moosges stand out.
Converted statemuni group
In total, 19 commonsense QA datasets contribute
Rubbor stamps provido way to rake wossages out.
Arable nathrr provide a way to make messages stand nut.
200k statement groups and 4400 statements the training set of VERA.
Bandages provide way rake
stand
tenderizers provide a way to nake ressages stand out.
AMguunemet falsehoods. Existing common- sense QA datasets are mostly manually constructed or assembled from standard school exams. A model trained on these datasets might overfit specific an- notation patterns from humans which may limit generalization. Therefore, we augment QA prob- lems with LM-generated answers and construct additional incorrect statements. Specifically, for a multiple-choice question, we use a small LM to sample 50 possible answers to the question, and select the 3 least probable answers with generation probability less than 0.15 (making these unlikely to be correct answers). This threshold is chosen based on manual inspection of examples. We observe generated answers with probability larger than 0.15 are more likely be plausible. We create LM-augmented falsehoods for the training set of commonsense QA datasets, as noted in Table 5 (appendix).
Table I: Conversions from original commonsense QA problems and knowledge basc entries to statement groups that are used for training.
many of them are in the format of multiple-choice QA (selecting the correct answer out of a set of choices) or boolean (yes/no) QA. These can be eas- ily converted to correct and incorrect commonsense statements. From multiple-choiie QA problems, we combine the question and each answer choice to form declarative statements, which are correct when using the correct answer, and incorrect oth- erwise. From boolean QA problems, convert the question into declarative statement, and keep the original label as the correctness label. Concrete examples can be found in Table 1.
small portion
Statement groups. We refer to statements orig- statement
inating from the same problem group. Note that statement groups originating from multiple-choice problems contain least two state- ments, of which one and only one is correct; state- ment groups originating from boolean problems contain only one statement, and it can be either correct or incorrect.
3.1.2 From Commonsense KBs
Commonsense KBs (e.g.. Atomic2020 in Hwang et al. (2020), and GenericsKB in Bhakthavatsalan et al. (2020)) contain a large number of correct commonsse statements. To create incorrect state- ments, we automatically perturb KB entries by re- placing the subject with three random subjects that appear in the KB. Table shows how to convert an entry in GenericsKB to a statement group contain- ing four statements, three of which are augmented via perturbations. The perturbed statements are relatively easy to identify and may contain false negatives. As noted in $3.2.4, we use these KB- constructed statements in separate training stage that precedes training with QA-constructed state- ments. In total, two commonsense KBs contribute ~ 1.66 statement groups and ~6M statements to the training set of VERA.
We do conversion to declarative statements auto- matically. From QA datasets, we create declarative statements from QA problems using the following method:
If the problem contains a question, we con- vert the question and choice into a declara- tive statement using the question conversion model created by Chen et al. (2021).
If the question is stoze-style, we replace the blank with the choice.
If the question is an incomplete sentence and

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3.2 Model Training
KB. To achieve this goal, we treat each state- ment group as a multi-class classification problem. maximizing the log-likelihood of the single correct statement in the statement group after passing the logits through a softmax. Formally,
3.2.1 Model Architecture
Given a statement x, VERA outputs a real-valued score 8 E [0, 1]. As we will use a transformer- based LM as the backbone of VERA, we first ex- tract the input representation h by selecting the last hidden state corresponding to the EOS input token. We choose EOS because it is capable to encode the entire input in both bidirectional encoder models (e.g.. T5's encoder) and left-to-right decoder mod- els (e.g.. LLaMA). Then a linear layer projects h to a scalar logit 2, followed by a sigmoid function (-) that transforms the logit into a score s. Formally,
Line = - log exp=((0;+))
Σ C == exp ((z)))
where T.j. is the correct statement in X-. Note that the multi-class loss not applicable to statement groups with only one statement (i.e., statement groups from boolean QA datasets). We empiri- cally find that the multi-class loss indeed improves generalization towards unseen multiple-choice QA datasets as indicated in Figure 3 (appendix).
h = fLM(a), = ftincar (h), =z()).
For brevity, we use hxx)), =xx) and s(x) to refer to the representation, logit and score of arbitrary input v.
Supervised contrastive loss. It has been shown (Khosla al., 2020) that supervised contrastive learning helps to improve model robustness and generalization against input variations. In light of this, we further adopt supervised contrastive learn- ing on top of the input representations h. We show in Figure 3 (appendix) that the contrastive loss in- deed improve generalization to unseen datasets. For each anchor statement 2; in batch, the con- trastive loss aims to maximize the similarity be- tween F, and each other statement Zp that has the same correctness label as 2; (i.e., positive example). At the same time, we push apart æ; and other state- ments &n that has opposite correctness label as x; (i.e., negative example). The supervised contrastive loss is
3.2.2 Batching
The data we construct consists of statements be- longing to different statement groups. For rea- sons we will describe in $3.2.3, we put all state- ments belonging to the same statement group into the same batch. Each batch may contain multiple complete statement groups. We denote by BG the number of statement groups and By the number of statements in total within a single batch. We denote the statement groups as (X,) BG and the statements as { { {m))/ss
partition of Us V/s E {0, 13 is the correctness label of xi-
3.2.3 Training Objectives
Leu
The model is trained with a linear combination of three losses: a binary classification loss, a multi- class loss, and a supervised contrastive loss, L = aLoin + 3m + Yeer, which we describe below.
ΣMEP( (xp)cos((((:)) h(z))) ELer(jUn explcos(h(2;)) h )
log
where 7 is a temperature hyperparameter, cos(:.) refers to cosine similarity, (i) C [Bs] is the index set of statements that are positive examples for z4, and (N()) S [Bs] is the index set of statements that are negative examples for 2;. Formally,
Binary classification loss. Naively, common- sense statement verification can be viewed as binary classification task. Under this setting, the loss is
P(1) (k: k < Bs, yk- Ve, k + i),
Loin = Yi log s(x;) - (1 yi) log(1 - s(r:)).
N(11 {k:
k < Bs, yk # vi).
We expect the model to be ro- bust against nuances in commonsense statements. Ideally, the model should be able to recognize oppo- site correctness labels for a group of seemingly sim- ilar statements in surface forms, such as statements created from different choices of the same question, or perturbed from the same piece of knowledge in
Multi-class loss.
3.2.4 Two-Stage Training
Since data sourced from KBs are larger in scale but more noisy than data sourced from QA datasets, we take a two-stage training approach. In training stage A, we start from a pre-trained LM and train with data sourced from KBs. In training stage B,

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we start from the model obtained in stage A and train with data soureed from QA datasets. During experiments we found that this setting is better than single-stage training with either data source or a mixture of the two.
2023), which is a left-to-right decoder model. For the T5 encoder, we start from the pretrained 15-v1.1-XX0 whose encoder has about 5B pa- rameters, and refer to the resulting model as VERA- T5. (During experiments we found that starting from F1an-T5-XXL performs slightly worse than starting from T5-v1. 1 1-XXL.) For LLaMA, we start from the pretrained LLaMA-7B and refer to the resulting model as VERA-LLaMA. As will see, VERA-T5 has better performance than VERA- LLaMA, so unless explicitly specified, when we say VERA we mean VERA-T5, See Table 8 (ap- pendix) for the complete hyperparameter settings and $C for the implementation details.
3.3 Inference and Calibration
An ideal plausibility estimation model should be calibrated, that is, its confidence in its predictions should be approximately equal to the actual fre- quency of correctness. During early experiments, we found that VERA tends be overconfident. Therefore, we apply a post hoc calibration on VERA'S 's output. Following the temperature scal- ing method introduced in Guo et al. (2017), during inference we divide the model preeil-pe logit by a temperature T before computing the score, that is,
4.2 Evaluation and Baselines
Evaluation protocol. We divide our evaluation into two parts: (1) Seen benchmarks, whose train- ing set is used for model training.
(L) 2 = fineur (h), = =/T,
(2) Unseen benchmarks, whose training set is not used for model training. We futher divide up the unseen benchmarks into type and rype 2, where in type benchmarks the task is similar to those in the seen benchmarks, while type benchmarks are a bit further away in terms of the nature of the task. Examples of type 2 unseen benchmarks include HellaSwag which is contextualized with event de- scriptions, and CREAK which involves reasoning among different entities.
Note that no temperature scaling is applied during model training.
With predictions on a validation set D {Z 12 we estimate T that gives the mini- mal expected calibration error (BCE) (Nacini et al.. 2015) on this validation set. Equation in SC.1 shows how ECE is computed. In practice, we use the combined development sets of the seen datasets ($4.2) to estimate T, and the optimal T becomes parameter of VERA. Note that temperature scaling does not change the relative ordering of prediction scores, and thus the other performance metrics (e.g.. accuracy) are not affected (see detailed explanation in $B.2).
Depending on the nature of the evaluation bench- mark, we use different metrics to evaluate our model's performance. Unless explicitly said other- wise, we report performance on the development set, where the gold labels are available, and we do not use the development sets of unseen datasets for model selection. The overall metric reported over multiple benchmarks the unweighted aver- age of the metric over all these benchmarks, which accounts for the differently-sized evaluation sets.
4 Experimental Setup
In this section, we provide more details of model training, the evaluation protocol and metrics, and describe the baseline models we benchmark.
4.1 Training Details
Datasets. For training stage A, we use the ~166 statement groups (~6M statements) sourced from two commonsense KBs; for training stage B, we use the 200k statement groups (~400k state- ments) sourced from 19 commonsense QA datasets. For each training stage, we mix the training sets of all datasets together, without any re-weighting.
Metrics. We report accuracy for multiple-choice and balanced boolean benchmarks. For those un- balanced boolean benchmarks (e.g., I.M-ennrated knowledge filtering datasets), we report area under the ROC curve (AUROC) and average precision (AP). To measure how well the model-predicted scores reflect confidence, we measure the ECE (Nacini et al., 2015) on the boolean benchmarks. following Equation 1.
Models. We use two types of pretrained LMS as the backbone of VERA: (1) the encoder of
T5 (Raffel et al., 2020), which is a bidirec- tional encoder model; (2) LLaMA (Touvron et al.,
hnttp::/huugiingaaee http://ggiiingga.ce.oooooooeooee co/google/t5-vi_I-w Pnttps://huggingface co/ /ooggl//laanttSxxll

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Accuracy
Seen
Unseen (type 1)
Unseen (type 2)
to predict correctness labels of statements. Table 2 reports the results when VERA is applied to solve commonsense problems. See Figure and Table 9, 10, 11 (appendix) for full results including AUROC and AP. On seen benchmarks (16 multiple- choice and one boolean), VERA outperforms the best baseline, Flan-T5, by 6% on (absolute) ac- curacy and 9% on AUROC. VERA beats Flan-T5 by 4% accuracy and 5% AUROC on type I un- seen benchmarks (four multiple-choice and one boolcan), and by 4% accuracy and 6% AUROC on type 2 unseen benchmarks (five multiple-choice and two boolean), demonstrating good generaliza- tion. VERA-T5 has better performance than VERA- LLaMA across the board, which may be due its bidirectional connectivity. Aside from perfor- mance, VERA also has good calibration, with ECE no higher than 3% on seen and unseen benchmarks. The post hoc calibration method improves calibra- tion across all three parts.
SKD Critic (355M)
36.64
38.34
43.40
12D2 Critic (355M)
55.03
54.79
67.11
UnifiedQA-v2 (IIB)
56.33
59.73
53.95
Entailer (11B)
73.79
71.47
70.72
GPT-3.5 (175B)
75.41
71.03
78.87
ChaiGPTT
62.11
61.20
62.83
GPT-41
72.35
77.40
70.29
Flan-TSt (1IB)
79.50
77.62
78.89
VERA-ILaMA (7B)
82.99
75.51
82.56
VERA-TS (5B)
85.51
81.65
83.37
Table 2: Results problem-solving with VERA on seen and unseen henchmarks. Average accuracy the development sets is reported. Accuracy across different parts (seen, unseen (type 1), unseen (type 2)) not directly comparable due to different underlying bench- marks. See Figure and Table 9, 10. (appendix) for full results. t: The performance of ChatGPT and GPT-4 may be under-estimated because we don't 't have access to the raw token logits. Flan-T5 has been trained on some unseen benchmarks use; see Table 7 (appendix) for details on data contamination.
Typically we may need to choose a threshold for binary classification in boolean datasets. However, we notice that a zero logit (= = 0) is generally close to the optimal decision threshold between correct and incorrect commonsense statements. Therefore do not estimate model-specific threshold, and simply use the default threshold: 0, equiva- lently, 0.5.
Baseline Models. We compare VERA with the best publicly available models that can be di- rectly used or repurposed for commonsense state- ment verification. Roughly in increasing order of performance, these models are: SKD Critic (West et al., 2021), 12D2 Critic (Bhagavatula et al., 2022), UnifiedQA-v2 (Khashabi et al., 2022), En- tailer (Tafjord et al., 2022), GPT-3.5 (OpenAI, 2022b), ChatGPT (OpenAI, 2022a), GPT-4 (Ope- nAL, 2023), and Flan-T5 (Chung et al., 2022). See more details SC.2.
5.2 Filtering LM-generated Commonsense Knowledge
Figure 2 reports the results when VERA is ap- plied to filter LM-generated commonsense knowl- edge. On the two seen benchmarks, SKD __ann_na____ and I2D2 2nno_anno VERA is better knowledge filter than all baseline models, in terms of both AUROC and AP. In particular, on 12D2 anno it outperforms the 12D2 critic model by 2% AUROC, which is specif- ically trained on the 12D2_anno dataset and does not generalize well to other benchmarks. On the unseen benchmark, Rainier anno, VERA is also comparable with the best baselines like Flan- and GPT-3.5. As for calibration, the ECE is no higher than 8% on all three benchmarks.
5 Evaluation Results
this section, we evaluate the ability VERA to estimate the plausibility of commonsense state- ments and compare it with the baseline models. We show the effectiveness of VERA in three sce- narios: solving commonsense problems, filtering LM-generated commonsense knowledge, and de- tecting commonsense errors in ChatGPT outputs.
5.1 Solving Multiple-Choice and Boolean Commonsense Problems
The output plausibility scores from VERA can be used for solving multiple-choice and boolean com- monsense problems. We first convert the prob- Jems into the statement group format ($3.1). For multiple-choice problems, we choose the statement with the highest score in the statement group. For boolean problems, we use s = 0.5 as the threshold
We find that filtering commonsense knowledge using VERA can greatly improve the performance of knowledge-augmentted reasoning methods. In the Generated Knowledge Prompting framework (Liu al., 2021), when solving a commonsense QA problem, first a knowledge model generates several commonsense knowledge statements rele-

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Generator Filter QA Model
Acc
Usefulness
random
Entaker (138)
Fan 15 (118)
5AD OHBE (255R)
CPT-3.5 (1758)
Viro-LaatA (78)
UnifiedQA
60.45
1402 CIRK (353M)
Viroltb 199)
Unliegghe (138)
CPT-4
GPT-3
UnifiedQA 67.44
+6.99
ROC Curve
Callaration Curve
GPT-3
VERA UnifiedQA 70.67
+10.22
+46%
1.0
3.0
UnifiedQA 60.45
SKD er.o (seen)
4.8
0066
Rainier
UnifiedQA 61.78
+1.33
5.0
0.6
Rainier
VERA
UnifiedQA 64.88
+4.43
+233%
0.4
10.4
0.2
Table Results of introducing VERA into the Gener- ated Knowledge Prompting pipeline (Liu et al., 2021). The QA model is unified@A-large, and the generator either GPT-3 (davinci) or Rainier-large when applicable. Average accuracy on the development set is reported; see Table 12 (appendix) for detailed results.
0.04
5.0
0.0
5.01
Rostivv Rate
Preducted Soore
1.0
Te0dbr Cannes Frollctions
2.0
1202 arnaaann (seen)
10.0
2.0
0.6
3.4
0.4
usefulness GPT-3's and Rainier's knowledge by 46% and 233%, respectively. VERA can effectively supervise and improve the quality of commonsense knowledge generated by much larger model, GPT-3 (davinci). Detailed results (Table 12, appendix) show that there is increased effectiveness in every individual benchmark.
0.0
0.0
1.0
1.0
3.0
Rainier, _anno tunseent
Feaclan of Carrent
0.6
8.6
0.6
ECE (4]
2.4
0.4
002
0.0
0.0
5.3 Preliminary Study on Detecting Commonsense Errors made by ChatGPT
0.0
0.4
0.6
2.0
0.2
0.5
3.0
Figure 2: Results for filtering LM- --ggenerated common- sense knowledge with VERA. We plot the calibration curve for both the uncalibrated version (w/ faded color) and calibrated version (w/ saturated color) of the VERA model. Results on the development sets are reported. See Figure for full results.
VERA can be useful in detecting commonsense mistakes made by generative LMs nn-the-wild, We collected 27 anecdotes from the Internet where people reported ChatGPT making commonsense errors, and manually rewrote them into their correct versions, obtaining 54 statements in total.
When detecting incorrect commonsense state- ments in this dataset, VERA has precision of 91% and a recall of 74%, amounting to an Fi score of 82%. Table 4 shows how VERA scores some of these these erroneous commonsense statements and their manually corrected version. In 7 out of the 9 cases, VERA assigns a low score to the original, in- correct statement, and high score to the corrected statement. For example, "since the density of a marble much less than the density of mercury, the marble would sink to the bottom of the bowl if placed in it" receives a score of 0.04 and is identi- fied as an incorrect statement, whereas "since the density of a marble is much less than the density of mercury, the marble would float if placed in mer- cury" receives a score of 0.96 and is identified as correct statement. Meanwhile, there are also some failure cases. VERA believes that "it is possible for solar eclipse to be followed by a lunar eclipse the next day", and fails to reject that "it is possible to draw a diagonal line in a triangle".
vant to the question, and then QA model makes predictions based on them. A big problem that hinders the effectiveness of this framework that model-generated knowledge is not always factual, and incorrect knowledge statements can mislead the QA model. We introduce VERA to filter these statements before passing them to the QA model. In particular, we keep those statements that receive score higher than 0.5 from VERA.
Following Liu et al. (2022b), we use UnifiedgA-large as the QA model, and consider two knowledge models: few-shot GPT-3 (davinci) (Brown et al., 2020) and Rainier-large (Liu et al., 2022b). We follow the evaluation settings as in Liu et al. (2022b), and for few-shot GPT-3 (davinci), we use the same task-specific few-shot prompts and same process to generate silver knowledge as in Liu et al. (2022b). Results are shown in Table 3. Applying knowledge filtering with VERA increases the

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Date
Original Corrected
Score Pred
is possible a solar eclipse followed by lunar eclipse the next day.
2023/01/05
is impossible for solar eclipse followed by a lunar eclipse next day.
The time it lakes for given numher of cars to travel fixed distance directly proportional to the number of cars, 202301/066 The time it takes for given number of cars to travel a fixed distance is invariant of the number of cars.
0.26
x
0.32
/
2023/11006 If A sits next to and B sits next to C, then A must sil next to C.
0.20
X
If A sits next to and B sits next to C, then A may not sit next to C.
0.60
food in minute, then would take If two cats eat two of food in minute, ihen would lake three cats three cans of food minule.
If two
three of food minute.
0.05
2023/01/10
0.67
three-dimensional
eight faces,
2023/01/11 A three-dimensional cube has six faces.
0.70
It is possible to draw diagonal line triangle.
0.80
/
202301/30 It is impossible to draw a diagonal line in a triangle.
0.28
X
70 is a smaller number than 58.
0.14
X
2023/02/21 70 is a larger number than 58.
0.85
/
Since the density of marble much less than the density of mercury, the marble would sink the bottom of the 2023/12/23 bowl placed Since the density of marble much less than the density of mercury, the marble would float placed mercury. 0.96
0.04
/
Both a house and a pound feathers weigh same, which is one pound.
2023/02/25 A house weighs more than one pound, while pound of feathers weighs one pound.
0.87
Table Examples of commonsense mistakes made by ChatGPT, and how VERA can detect them. each section, the first line is the original, incorrect commonsense statement in ChatGPT's output, and the second line is the authors' manually corrected version of the statement. Each statement followed by VERA's 's score and predicted correctness label. Examples are adapted from Venuto (2023); Marcus and Davis (2023); Borji (2023).
unseen (type
Vera-T5 (58)
Accuracy
Accuracy
0.90
Contrastive loss
Training stage
0.85
0.7
LM augmented falsehoods
0.80
Multi-class loss
0.6
0.75
Binary loss
multiple-choice
boolean
multiple-choice
Figure 3: Ablation results. Average accuracy on the development sets is reported. Components incrementally removed from the training process, except for the multi-class loss and the binary loss; the hierarchy is indicated in the legend.
5.4 Analysis
emphasizing the importance of training with large- scale commonsense knowledge. LM-augmented falsehoods are most helpful on unseen benchmarks, with little sacrifice in the performance on seen benchmarks. The multi-class loss is most helpful on multiple-choice benchmarks, while removing the binary loss substantially hurts performance on boolean benchmarks.
Ablations. We conduct an ablation study by incrementally removing the following compo- nents from the training process: contrastive loss ($3.2.3), training stage A ($3.2.4), LM- auggenedd falsehoods ($3.1), multi-class loss or binary loss ($3.2.3). Since at least one of the multi-class loss and the binary loss needed, we remove them sep- arately and observe the effect of training with a single loss.
Scaling Trends of VERA. We trained variants of VERA that are based on smaller versions of the T5 encoder, and show the results in Figure 4. Model performance increases steadily with size, and does not show evidence of saturation at 5B parameters, suggesting that better commonsense plausibility estimation models might yielded from larger pretrained LMs.
Results are shown in Figure 3. Overall, the ab- lated components have more impact on unseen benchmarks than seen ones. Removing the con- trastive loss hurts performance mostly on unseen datasets, implying that the contrastive objective is beneficial for generalization. Removing train- ing stage A hurts performance across the board,

---

(lype 1)
0.8
Vera-T5-small (40M)
Accuracy
Accuracy
0.8
Vera-T5-base (100M)
0.7
Vera-T5-large (300M)
0.6
0.6
Vera-T5-xl (1B)
Vera-T5-xxl (5B)
0.5
multiple chiicce boolean
multiple choice
boolean
Figure 4: Scaling trends of commonsense statement verifiers.
potheses to verify and are typically complemented with additional context. In contrast, we focus how this format compares with the QA format is provided in $E and Figure
Format: Verification vs. QA. In this paper, we focus on the verification format to solve common- sense problems. A comprehensive discussion on
on verifying standalone commonsense statements
where no context is required provided.
Generation vs. verification. With the rapid progress in generative LMs, researchers have been largely building methods with a generative approach (Khashabi et al., 2020, 2022; Lourie et al., 2021; Tafjord and Clark, 2021; Wei et al., 2022). However, current generative LMs still prone to hallucination rors and lack intrinsic mechanism express confidence level on their outputs. Verification, on the other band, shows promise to complement these shortcomings and has been adopted to improve the outcome of generation (Chen et al., 2021; ; Jiang et al., 2022). In this work, we take a pure verifica- tion approach and build general- for commonsense statements, which to our best knowledge is the first of its kind.
6 Related Work
problem-solving
Commonsense verifiers. Prior work has ex- plored the idea of verifying commonsense state- ments. SYMBOLIC KNOWLEDDG DISTILLATION (West et al., 2021) and I2D2 (Bhagavatula al., 2022) train models classify the acceptability of model-generated commonsense statements. The ENTAILER (Tafjord et al., 2022) model is partially trained to score the validity of a given hypothesis. These models are trained relatively small-scale, dpeain- data and do not generalize well to broader commonsense domains. Some other work uses pretrained LMs with few-shot prompting to verify commonsense statements (Kadavath al., 2022: Jung et al., 2022). In this work, we develop a general-purpose commonsense statement verifier that works out-of-the-boo in zero-shot setting.
verifier
Conclusion and Future Work
We introduced VERA, a general-purpose verifica- tion model for commonsense statements and an early step toward tools for mitigating common- errors in text generated by language models. VERA achieves state-of-the- art performance when solving commonsense problems in the verification format, excels filtering LM AM--enerated common- sense knowledge statements, and found useful 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 state- ments if needed. As VERA mainly targets single- sentence statements, future work may consider ver- ification of multi-sentence or form-form statements, or contextualized/defeesiilll commonsense state- ments.
Verification in other tasks. Beyond common- sense statements, the problem of verification has been extensively studied on various NLP tasks. NLI (Liu al., 2019, 2022a; Zhang al., 2017) can be viewed as an entailment verification task. Chen al. (2021) presents method for QA ver- ification by transforming the context passage and question-answer pair into a premise-hypothesis for- mat as in NLI. Some work build models to per- form reasoning verification classifying whether a premise supports or refutes hypothesis (Bostrom et al., 2022; Sprague et al., 2022; Yang al., 2022; Tafjord et al., 2022). On the other hand, fact veri- fication (Thorne al.. 2018; Wadden et al., 2020) requires judging the validity claims against cor- pus of evidence (c.g., Wikipedia). These tasks fea- ture context-semsitive or knowledge-intensive hy-

---

Limitations
Sumithra Bhakthavatsallam, Chloe Anastasiades, and Peter Clark. 2020. Genericskb: A knowledge base of generic statements. ArXiv, abs/2005 Nos/2005.00660.. 00666).
VERA aims, and is trained, to predict the plausibil- ity of statements based on objective commonsense knowledge of our world. is not intended to handle text outside the scope of commonsense statements (e.g.. encyclopedic facts, reading comprehension with fictional worlds). It is not trained or evaluated on moral commonsense data, so its capability of making moral predictions is unknown. It gives a prediction even if the input falls out of its intended scope, which could be mitigated by an additional scope guard to determine its applicability. In ad- dition, it is not trained to handle very long and compositional input. Although greatly outperform- ing existing systems, VERA make incorrect predictions. It is not very robust under syntactic variations of the input, such as para- phrases and negations. As the training data may contain bias or toxicity, VERA may also make pre- dictions that are perceived as ethically problematic. The output of VERA does not reflect the authors' view. VERA is research prototype, and it is not designed for making real-world decisions.
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2019. Piqa: Reasoning about physical commonsense in natural language. ArXiv,
abs/1911.11641.
Ali Borji. 2023. A categorical archive of chatgpt fail- ures. ArXiv, abs/2302.03394.
Kaj Bostrom, Zayne Sprague, Swarat Chaudhuri, and Greg Durrett. 2022. Natural language deduction through search over statement compositions. In Con- ference on Empirical Methods in Natural Language Processing.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler. Jeff Wu. Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark. Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. ArXiv, abs/2005.14166
perfect and may
Acknowledgments
Jifan Chen, Funsol Choi, and Greg Durrett. 2021. Can nli models verify qa systems' predictions? ArXiv. 0bs/2104.0877.
We thank Sean Welleck, Peter West, Alisa Liu, Jae- hun Jung, Chandra Bhagavatula, Ram Pasunuru, Asli Celikyilmaz, and members of the H2lab, Xlab and ARK lab for their discussion and construc- tive feedback. This work was funded in part by the DARPA MCS program through NIWC Pacific (N66001-19-2-4031,, NSF IIS-2044660, and ONR N00014-18-1-2888 We thank OpenAl for offering access to their API.
Michael Chen, Mike D' Arcy, Alisa Liu, Jared Fernan- dez, and Doug Downey. 2019. Codah: An adversar- ially authored question-anse answer dataset for common sense. arXiv preprint arXiv: 1904.0490.433
Hyung Won Chung, Le Hou, S. Longpre, Barret Zoph, Yi Tay. William Fedus, Eric Li, Xuezhi Wang. Mostafa Dehghani, Siddhartha Brahma, Albert Web- son. Shixiang Shane Gu, Zhuyun Dai, Mirac Suz- gun, Xinyun Chen, Aakanksha Chowdhery, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Wei Yu, Vincent Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed Huai hsin Chi, Jeff Dean, Jacob Devlin, Adam Roherts, Denny Zhou, Quoe V. Le, and Jason Wei. 2022. Seal- ing iestructionfinettune language models. ArXiv. abs/2210. 1416.
References
Stephane T Aroca-Ouellette, Cory Paik, Alessandro Roncone, and Katharina Kann, 2021. Prost: Physical reasoning about objects through space and time. In Findings.
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question an- swering? try arc, the ai2 reasoning challenge. ArXiv, abs/1803.05557.
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Han- nah Rashkin, Doug Downey, Scott Yih, and Yejin Choi. 2019. Abductive commonsense reasoning. ArXiv, 1bs/1908.05739.
Benchmarks for automated Ernest Davis. 2023. commonsense reasoning: A survey. ArXiv, abs/2302.04752.
Chandra Bhagavatula, Jena D. Hwang. Doug Downey, Ronan Le Bras, Ximing Lu. Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, and Yejin Choi. 2022. 12d2: Inductive knowledge distil- lation with neurologic and self-imitation. ArXiv, abs/2212.09246.
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Roth, and Jonathan Berant. 2021. Did aristotle use a laptop? question answering benchmark with

---

implicit reasoning strategies. Transactions of the Association for Computational Linguistics, 9:346- 361.
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishman. 2020. Su- pervised contrastive learning. In Advances in Neural Information Processing Systems, volume 33.
Andrew S. Gordon, Zomitsa Kozareva, and Mclissa Roemmele, 2011 I. Semeval-2012 task 7: Choice of plausible alternatives: An evaluation of common- sense causal reasoning. In International Workshop on Semantic Evaluation.
Tushar Khot, Peter Clark, Michal Guerquin, Pe- ter Alexander Jansen, and Ashish Sabharwal. 2019. Qase: A dataset for question answering via sentence composition. ArXiv, abs/1910.11473.
Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, and Sourab Mangrulkar. 2022. Accelerate: Training and inference at scale made simple, efficient and adaptable. https:// git thub. com/hugging awepii/faaceaaceeerrtttee
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR, abs/1412.6980.
Hector J. Levesque. Ernest Davis, and L. Morgenster. 2011. The winograd schema challenge. In Interna- rional Conference on Principles of Knowledge Rep- resentation and Reasoning.
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Wein- berger. 2017. On calibration of modern neural net- works. In International Conference on Machine Learning.
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xi- ang Ren. 2020. Birds have four legs?! numersense: Probing numerical commonsense knowledge of pre- trained language models. ArXiv, abs/2005.000 006633.
Jena D. Hwang. Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2020. Comet-atomic 2020: On symbolic and neural commonsense knowledge graphs. In AAA! Conference on Artificial Intelligence.
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. TruthfulQA: Mcasuring how models mimic human falsehoods. In ACL, pages 3214-3252.
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li. Jiacheng Liu, Mateja Jamnik, Timo- thée Lacroix, Yuhuai Wu, and Guillaume Lam- ple. 2022. Draft, sketch, and prove: Guiding for- mal theorem provers with informal proofs. ArXiv, abs/2210.12288.
Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi. 2022a. Wanli: Worker and ai collabora- tion for natural language inference dataset creation. In Conference on Empirical Methods in Natural Lan- guage Processing.
Jachun Jung, Lianhui Qin, Sean Welleck, Faeze Brah- man, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022. Maieutic prompting: Logically consistent reasoning with recursive explanations. In Conference on Empirical Methods in Natural Lan- guage Processing.
Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannanch Hajishirzi, and Yejin Choi. 2022b. Rainier: Reinforced knowledge introspector for commonsense question answering. In Conference Empirical Methods in Natural Language Process- ing.
Saurav Kadavath, Tom Conerly, Amanda Askell, T. J. Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zachary Dodds, Nova DasSarma, Eli Tran- Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage. Tristan Hume. Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, John Kernion, Shauna Kravec, Liane Lovitt. Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Benjamin Mann, Sam McCandlish, Christopher Olah, and Jared Kaplan. 2022. Language models (mostly) know what they know. ArXiv, abx//207.0s 00.221..
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Pe- ter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2021. Generated knowledge prompting for commonsense reasoning. ArXiv, abs/21 100.08877
Xiao Liu, Da Yin, Yansong Feng, and Dongyan Zhao. 2022c. Things not written in text: Exploring spatial commonsense from visual signals. ArXiv, abs/2203.08075.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. ArXiv, abs//190..11 16992.
Daniel Khashabi, Yeganeh Kordi, and Hannaneh Ha- jishirzi. 2022. Unifiedqa-v2: Stronger general- ization via broader cross-format training. ArXiv, 12359
Nicholas Lourie. Ronan Le Bras, Chandra Bhagavat- ula, and Yejin Choi. 2021. Unicorn on rainbow: A universal commonsense reasoning model on new multitask benchmark. AAAI Conference on Artifi- cial Intelligence.
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sah- harwal, Oyvind Tafjord, Peter Clark, and Hannanch Hajishirzi. 2020. Unifiedqa: Crossing format bound- aries with a single qa system. In Findings.

---

Gary Mareus and Emnest Davis. 2023. Chat- gpv/lm errors (public). https:/ docs. goo.ooo..............// wttps//aaaasieeee///////or/rrrre bxH5ga4222KKoo00000rrrrricddeii
Oyvind Tafjord, Peter Clark, Matt Gardner, Wen tau Yih, and Ashish Sabharwal. 2018. Quarel: A dataset and models for answering questions about qualitative relationships. ArXiv, abs/181 181 1.08048.
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. Can a suit armor conduct elec- tricity? new dataset for open book question answer- ing. In Conference on Empirical Methods in Natural Language Processing.
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2022. Entailer: Answering questions with faithful and truth- ful chains of reasoning. Conference on Empirical Methods in Natural Language Processing.
Oyvind Tafjord, Matt Gardner, Kevin Lin, and Peter Clark. 2019. Quartz: An open-domain dataset of qualitative relationship questions. ArXiv, hts/1909.03550
N. Mostafazadeh, Nathanael Chambers. Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Push- meet Kohli, and James F. Allen. 2016. A corpus and cloze evaluation for deeper understanding of com- monsense stories. In North American Chapter of the Association for Computational Linguistics.
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. Commonsenseqa: A question answering challenge targeting commonsense knowl- edge. ArXiv, 0bss/8811.0092
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht. 2015. Obtaining well calibrated proba-
bilities using bayesian binning. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Conference Artificial Intelligence, 2015:2901- 2907.
Alon Talmor, Oni Yoran, Ronan Le Bras. Chan- drasekhar Bhagavatula, Yoav Goldberg, Yejin Choi, and Jonathan Berant. 2021. Commonsenseqa 2.0: Exposing the limits of ai through gamification. ArXiv, abs/2201.055 .05320.
Yasumasa Onoe, Michael J.Q. Zhang. Eunsol Choi, and Greg Durrett. 2021. Creak: A dataset for com- monsense reasoning over entity knowledge, ArXiv, abs/2109.01653.
James Thorn. Andreas Vlachos. Christos Christoddoulopoulos,, and Arpit Mittal. 2018. Fever: large-scale dataset for fact extraction and verification. ArXiv, abs/l803.00 100./85535355..
OpenAI. 2022a. Introducing chatgpt.
OpenAL. 20226. Models overview gpt-3.5.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aur' elien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. ArXiv. abs/2302.13971.
OpenAT. 2023. Gpt-4 technical report. Technical report, OpenAT.
Colin Raffel, Noam Shazeer, Adam Roberts, Kather- ine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with unified text-to to-tto-text transformer. Journal of Machine Learning Research,
Giuseppe Venuto. 2023. chalgptsfailures. https:// gi github.co comub.ccom/eee wwt. http://ww///hhtgttt-aaaaa fails/
11(140):1-67.
David Wadden, Kyle Lo, Lucy Lu Wang, Shanchuan Lin, Madeleine van Zuylen, Arman Cohan, and Han- naneh Hajishirzi. 2020. Fact or fiction: Verifying scientific claims. ArXiv, abs/2004.14974.
Keisuke Sakaguchi. Ronan Le Bras, Chandra Bhagavat- ula, and Yejin Choi. 2019. Winogrande: An adver- sarial winograd schema challenge at scale. Commun. ACM, 64:99-106.
Cunxiang Wang, Shuailong Liang, Yili Jin, Yilong Wang, Xiaodan Zhu, and Yue Zhang. 2020. Semeval- 2020 task 4: Commonsense validation and explana- tion. In International Workshop on Semantic Evalua- tion.
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019. Social iqa: Common- sense reasoning about social interactions. ArXiv, abs/1904.09728.
Shikhar Singh, Nuan Wen, Yu Hou, Pegah Alipoormo- labashi, Te-Lin Wu, Xuezhe Ma, and Nanyun Peng. 2021. Com2sense: A commonsense reasoning bench- mark with complementary sentences. In Findings.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, Quoc Le, and Denny Zhou. 2022. Chain of thought prompting elicits reasoning in large language models. ArXiv, abs/2201.119053
Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett. 2022. Natural language deduction with incomplete information. In Conference on Empirical Methods in Natural Language Processing.
Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017. Crowdsoureing multiple choice science questions. ArXiv, abs/ abs/1707.002099
Oyvind Tafjord and Peter Clark. 2021. General- purpose gusstion-answering with macaw. ArXiv, abs/2109.02593.
Peter West, Chandrasekhar Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang. Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2021. Symbolic

---

knowledge distillation: from general language mod- els to commonsense models. North American Chapter of the Association for Computational Lin- guistics.
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019. Huggingface's 's transformers: State-of-the-Aa natural language processing. ArXiv, abs/1910.03771.
Kaiyu Yang, Jia Deng, and Danqi Chen. 2022. Gen- crating natural language proofs with verifier-guided search. In Conference on Empirical Methods in Nat- ural Language Processing.
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018. Swag: A large-scale adversarial dataset for grounded commonsense inference. In Conference on Empirical Methods in Natural Language Process- ing.
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Farhadi, and Yejin Choi. 2019. Hellaswag: Can a ma- chine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics.
Sheng Zhang, Rachel Rudinger, Kevin Duh, and Ben- jamin Van Durme. 2017. Ordinal common-sense inference. Transactions of the Association for Com- putational Linguistics, 5:379-395.

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A More Details on Datasets
I2D2 (annotated). The annotated dataset of I2D2 contains LM-generated commonsense statements with human-annotated correctness labels. use the combination of annotated data in "IterO" and "Iter2", because the data "Iterl" is missing from the website.
Table 6 shows more dataset statistics, and Table 7 shows the dataset citations and links from which we retrieved the datasets.
A.1 Dataset-Specific Special Handling
A.2 Conversion to Declarative Statements
For some datasets, we pre-process them into uni- fied multiple-choice boolean format. We provide the details below.
From QA datasets, we create declarative statements from QA problems using the following method:
If the problem contains question, con- vert the question and choice into declara- tive statement using the question conversion model created by Chen et (2021).
Com2Sense (paired). Com2Sense contains true and false statements that can be paired into com- plements. To utilize this pairing information, place the two statements in each pair into the same statement group, and treat this multiple-choice dataset. Some statements in the dev set are not paired, so we discarded these examples.
If the question is cloze-style, we replace the blank with the choice.
If the question is an incomplete sentence and the choice is continuation it, concate- nate the question and the choice.
CycIC (mc). CycIC contains both multiple- choice and boolean QA problems. To keep con- sistency in evaluation, we use only the multiple- choice problems, which is the dominant problem type in this dataset.
If there is no question and the problem only asks to choose between some choices, we use the choice as the declarative statement.
For boolean problems, we always use yes the choice and create a single declarative state- ment for each problem. We use the original label as the correctness label of this statement.
Com VE (task A). ComVE contains data for three tasks. Task A is assigning true/false labels to paired statements, similar to Com2Sense (paired).
Task B and C are about choosing and generating explanations to a given statement being against commonsense. We use the data for task A.
B More Details on Method
B.1
Training Objectives
SKD (annotated). The annotated dataset of Symbolic Knowledge Distillation (SKD) contains LM- LM-generaeed, semi-structured knowledge triples, where the head and tail events connected by relations, such
Binary classification loss. classification loss as
We defined the binary
Lbin (Ii, y1)
ys lol sxx;)) (1 y:) log(1 s(x;)).
To account for the fact that there are usually more incorrect statements than correct ones in the data produced from multiple-choice datasets, vide this loss by the number of statements with the same correctness label in the same statement group. Therefore, the binary classification loss for the whole batch
(PersonX doesn't t like to wait, xIntent, to get the job done).
di-
Following West et al. (2021), replace the name placeholders with random person names, and con- vert into natural language statements using tem- plates adapted from Hwang et al. (2020). For ex- ample, the triple in the above example becomes
Lhin
BG
1
Σ
Σ
- [lje Loin (Ijes Uje)
Arnold doesn't like to wait Because Arnold wanted get the jab done.
BG
Le Myje v)
C
VE(011
where Cy is the number of statements in statement group Xj, Tje the cth statement in Xj. and Il is the indicator function.
We set the correctness label to be true the valid field has a positive value.

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Multi-class loss. We defined the multi-class loss
moderate hyperparameter tuning, and did not do extensive search due to training cost. For tokenization, the T5 tokenizer tokenizes in- put so that it ends with the EOS token 
 (token ID = I). We manually configured the LLaMA tok- enizer so that its output ends with the EOS token 
 (token ID = 2), and does not contain the BOS token 
 (token ID 1). Models are trained for S = 50k steps with Bg 64 statement groups per batch, using the Adam optimizer (Kingma and Ba, 2014) with learning rate 7)
exp (1rz+) Ee expzzxjee
C = log
The multi-class loss for the whole batch is
1 Bg Lmo (Xz).
mm
BG
10 for T5 encoder and 7 2 x 10-6 for LLaMA. We train models with the Huggingface Transformers and Accelerate libraries (Wolf et al., 2019; Gug- ger et al., 2022). For memory efficiency, during training, each statement is truncated to 128 tokens (which can accommodate more than 99% of the statements; see Table 6) and each statement group capped to four statements.
Supervised contrastive loss.
We defined the su-
pervised contrastive loss
coe(Ries),bberr Lei((14y31) log- EKEP()) e
ΣKEP(JUN())
The supervised contrastive loss for the whole batch is
Bg (as, Wi)-
C.1 Definition of Metrics
Leu
Bs
Multiple-choice accuracy. For multiple-choice benchmarks, we report the multiple-choice accu- racy:
B.2 Calibration
Our calibration is a post-hoc strategy and does not affect the task performance metrics we report in $5. This is because applying our calibration method temperature scaling does not affect the relative order of plausibility scores assigned to given set of statements:
Accme
Maa = arg max s(xje)]. ayεx,
|D]
X,ED
Boolean accuracy. fined as
The boolean accuracy is de-
ACCbool
Σ I[ = I[z(x;) > o].
For tasks with multiple-choice questions ($5.1), calibration does not affect the argmax prediction for the above reason.
D
Boolean accuracy is applicable to balanced boolean benchmarks where there are roughly equal true and false statements (e.g., CommonsenseeA 2.0, Spatial Commonsense, Strategy CREAK), Generally it is not a good metric for multiple- choice benchmarks and unbalanced boolean bench- marks.
For commonsense knowledge filtering ($5.2), calibration does not affect the TPR/FPR num- bers at each corresponding decision point, again for the above reason, so the ROC curves are valid.
For True/False judgment problems ($5.1 and $5.3), calibration does not move the plausibil- ity scores across the decision boundary. We use logit 0.0 (or equivalently, plausibility score 0.5) as the True/False boundary. A positive (or negative) logit remains positive (or negative) after applying the temperature.
AUROC and AP. For unbalanced boolean bench- marks (e.g.. LM--gnnrated knowledge filtering datasets), accuracy may not faithfully capture the model's 's performance. Instead, the metrics we use are the area under the ROC curve (AUROC) and the average precision (AP) for selecting the Truc statements. Statements are ranked based on their signed raw scores, that different score thresholds can be selected to construct the ROC and Precision- Recall curves. Aside from unbalanced boolean benchmarks, AUROC and AP are also applicable
C More Details on Experimental Setup
Table 8 shows the hyperparamter settings for train- ing VERA. These values are obtained from some

---

multiple-choice and balanced boolean bench- marks.
or "no". We use this feature of the model and make act as commonsense statement verifier. For an input statement, we compute the logits received by "yes" and "no" in the decoder, denoted as and f, respectively. We let the logit & = t -- f and the score = a(t - f). We use the largest version of this model, Uni 1oifieddA-v2-11b."
Calibration. To measure how well the verifier- predicted score reflects its confidence, we measure the ECE (Nacini et al., 2015) on the boolean bench- marks. ECE is computed
M
Bml
Entailer. Entailer (Tafjord et al., 2022) is model trained to construct proof trees for scientific com- monsense hypotheses. This multi-angle model can be used in three ways: (1) given a hypothesis, generate a set of premises that may entail it; (2) given a hypothesis, predict score that reflects the model's belief in it; (3) given a hypothesis and set of premises, predict score that reflects whether there is valid entailment between them. We use (2) as a commonsense statement verifier. The model predicts [0, 1] score which use the final score, and we let the logit 2 (s). We use the largest version of this model, Entailer-11b.
ECE =
Acc( (Bm) Score(
D
M
|mml
1
Ty =
[D]
[mml
m=
s(2:) (E
(1)
[Bml
where M is the number of bins which bucket data points with similar predictions, and Bm S D is the subset data points that fall into the m-th bin. We use M = 10 equal-sized bins when computing ECE.
GPT-3.5. GPT-3.5 (OpenAI, 2022b) is a series of general-purpose autoregressive decodcr-only LMs. To make it act as a commonsense verifier, we use the following input prompt:
C.2 Details on Baseline Models
SKD Critic. West et al. (2021) trained critic model that filters incorrect commonsense knowl- edge generated by their symbolic knowledge distil- lation (SKD) method. This critic model is based on RoBERTa-large (Liu et al., 2019) and is finetuned on 8k GPT-3-generated commonsense knowledge sentences with human-annotated true/false labels. The model predicts a [0, !] score s which we use the final score, and we let the logit z o o (s).
Question: Based on commonsense knowledge, the Following
statement correct? answer yes no.
Statement: (statement)
Answer:
We query the OpenAT Completions API with this prompt and compute the logits received by Yes" and " No" in the next-token prediction, denoted as t and f, respectively. We let the logit = t t- and the score s = a(t f)- We experimented with several prompt formats and found the one presented above to have the best performance, and in most cases, Yes"" and No" together receive most of the probability mass during next-token prediction. We also experimented with several models the GPT-3 (Brown al., 2020) and GPT-3.5 series, and found GPT-3.5 ftext-davinci-0e22 to work the best.
I2D2 Critic. Bhagavatula et al. (2022) trained a critic model that filters incorrect commonsense knowledge generated by their 12D2 method. This critic model is based on RoBERTa- larrge (Liu et al., 2019) and is finetuned on 12k 12D2-generated commonsense knowledge sentences with human- annotated true/false labels. Given an input state- ment, the model predicts two logits: for the True label and f for the False label. We let the logit = = t - f and the score s = att - f). We use the critic model trained in the final iteration (i.e., "Iter 2" in 12D23).
UnifiedQA-v2 UnifiedQA-v2 (Khashabi al., 2022) is a general-purpose QA model trained on datasets with a variety of input formats, including boolcan datasets. When the input is a declarative statement, the model is trained to output either "yes"
Additionally, report a baseline where the (negated) language modeling perplexity used for
uni 1o
"nttps://hugginggacceee httpr//gawwa....aaaa//////e/aeaa
"https://platfomnn openai com/docs/
hnttps://gengen.aat apps. al allenai.orr
api opi-rrefrrence/cooleetiiooo

---

commonsense plausibility. Note that the plausibil- ity scores derived this way are normalized, and we only use them for ranking purposes. For this baseline, we use GPT-3.5 as the base model, and name it as "PPL. (GPT-3.5)"
D More Evaluation Results
Figure 5 is an expansion of Table and addition- ally shows the precision-recall curves on problem- solving benchmarks. Table 9, Table 10, and Ta- ble 11 show the per-dataset breakdown of the accu- racy numbers in Figure 5. Figure 6 is expansion of Figure 2 and additionally shows the precision- recall curves on knowledge-filee filtering benchmarks. Table 12 shows the per-datase breakdown of the accuracy numbers in Table 3.
ChatGPT and GPT-4. ChatGPT (OpenAI, 2022a) and GPT-4 (OpenAl, 2023) are optimized for chat. To make them act a commonsense veri- fier, we use the same input prompt as for GPT-3.5, without the "Answer:" " line. We query the OpenAI Chat APT with this prompt in a user message, and obtain the first token of the assistant message in the response. Besides this zero-shot setting, we ad- ditionally report a few-shot chain-of-thought (Wei et al., 2022) setting with 5 in-domain examples, for- matted additional uscr-assistant message pairs prior to the query user message.
E Further Analysis
Verification vs. QA. In this paper, we have been using the verification format to approach problem-sol tasks. But do we lose something when compared to using the QA format? Fig- ure 7 we compare how well existing models can solve problems in the verification format and the QA format. Verification format does fall behind QA format, especially with models trained exclu- sively in QA format (i.e., UnifiedQA-v2), We also trained sequence-to-sequeene
Format:
Since the API does not provide token logits, we let the score 8 = 1.0 when this token is "Yes", and = 0.0 when this token is "No". In the un- likely case that this token is neither, we let 0.5. We add small random noise to the score. This is arbitrate potentially multiple positive predic- tions within statement groups from multiple-choice QA problems, and to enable plotting the ROC and precision- curves. Note that this is not an ideal solution and may cause under-estimation of ChatGPT and GPT-4's performance.
model in QA for- mat the same multiple-choice data VERA. leads VERA by 1.5% on seen multiple-choice benchmarks. We hypothesize that this is because verification models only see one option at a time, whereas QA models can see all choices of prob- lem at the same time and thus can do comparative ranking.
In addition to the performance loss, verification model does lose the generative capability possessed by some QA models that are generative (c.g., Uni- fiedQA in Khashabi al. (2020)), and has to run C times to solve a C-way multiple-choice problem, whereas QA models (e.g.. UnifiedQA in Khashabi et al. (2020), Unicorn in Lourie et al. (2021)) need to run only once.
Flan-T5. Flan-T5 (Chung al., 2022) is series of sequence-to-sequenee LMs cestuuctionfinetunee massive number of tasks. To make it act as a commonsense verifier, we use the same input prompt as for GPT-3.5. We compute the logits received by "yes" and "no" in the first token predic- tion in the decoder, denoted as and f. respectively. We let the logit = L-f f and the score We experimented with several prompt formats and found the one presented above to have the best performance, and in most cases, "yes" and "no" to- gether receive most of the probability mass during the token prediction. We use the largest version of this model, Fl F1an-T5-XXL. 8 Note that some unseen benchmarks are in the training data of Flan-TS T5; see Table 7 for details on data contamination.
However, verification models can perform some tasks that generative QA models cannot cover. They can classify the correctness of declarative statements, without having to convert them into questions the first place. They can also reflect on the answer produced by a generative QA model, and provide a level of confidence. We argue that verification models and generative QA models have different best-application scenarios and are some- times complementary each other.
ol--)).
hhttps://platfor www.ppppattoorooooo......aa api-refference/chat

---

Abbr.
Name
Domain
Format
Train Ex. Aug # DDe Ex. Statements
True # False
STAGE A TRAINING
Atomic2020
Atomic2020
multiple-chrice (4)
803541
70731
282924
70731
212193
Ge
GeneriesKB
mulliple-chnice (4)
775820
96977
387908
96977
290931
1579361
167708
670832
167708
503124
STAGR B TRAINING (SREN)
OBQA
OpenBookQAA
scientific
multiple-chnice (4)
4957
500
2000
500
1.500
ARC
ARC (easy)
scientific
mulliple-chnice (4)
2251
570
2281
570
1711
ARC
(hard)
scientific
mulliple-chnice
A12Sci_e
A12 Science (elem)
scientific
multiple-chrice
623
123
489
123
366
A128ci_n m
AI2 Science (middle)
scientific
multiple-chnice (4)
605
125
S02
125
377
CSQA
CommonsenseQA
general
multiple-choice
9741
1221
6099
1221
4878
QASC
QASC
scientific
multiple-choice
8134
926
7408
926
6482
PIQA
Physical IQA
physical
multiple-choice (2)
16113
1838
3676
1838
1838
SIQA
Social IQA
social
multiple-chnice (3)
33410
1954
5861
1954
3907
Winogrande
general
multiple-chnice
1267
2534
1267
1267
C2S
Com2Sense (paired)
general
multiple-chnice
804
391
782
391
391
SciQ
SciQ
scientific
mulliple-chrice (4)
11679
1000
4000
1000
3000
QuaRel
QuaRel
qualitative
multiple-chnice (2)
1941
278
$56
278
278
QuaRTz
QuaRTz
qualitative
multiple-chnice
384
384
CycIC
CycIC (mc)
general
multiple-choiee
6521
907
4535
907
3628
ComVE
ComVE (task A)
general
multiple-choice (2)
10000
997
1994
997
997
CSQA2
CommonscnseQA 2.0
general
boolean
9264
2541
2541
1225
1316
SKD
SKD (annotated)
boolcan
7980
1015
1015
803
212
12D2 anno
12D2 (annotated)
boolean
26206
13094
13094
6158
6936
Total
194442
29430
61329
20966
40363
EVALUATION (UNSEEN TYPE 1)
WSC
WSC
general general quantitative physicul physicul
multiplo-choice
5466
273
273
COPA
COPA
multiplc-choiee (2)
0
500
1000
500
500
NumerSense
NumerSensc
multiple-choice (11)
0
200
2200
200
2000
PROST
PROST
multiplo-choice (4)
18736
74944
18736
56208
SpatialCS
Sputial Commonseuse
boolean
1448
1448
724
724
Rainier_ame anno
Rainier (annotated)
boolean
591
591
424
167
Total
21748
80729
20857
59872
EVALUATION (UNSEEK TYPE 2)
SWAG
SWAG
multiple-choice
20006
80024
20006
60018
HellaSwag
HellaSwag
multiple-choice (4)
0
10042
40168
10042
30126
CODAH
CODAH
multiple-choice (4)
2776
11104
2776
8328
SCT
Story Test
multiple-chuice
1871
3742
1871
1871
oNLI
aNLI
multiple-chocee
1532
3064
1532
1532
QtrategyQA
StrategyQA
boolean
0
229
229
107
122
CREAK
CREAK
boolean
1371
1371
691
680
Total
37827
139702
37025
102677
Table 5: Datasets and statistics. Data sourced from commonsense KBs are listed under STAGE A TRAINING, and data sourced from commonsense QA datasets are listed under STAGE B TRAINING. The number in parentheses under the Format column represents the number of choices per question. The Aug column indicates whether LMMaugmented falsehoods generated for each dataset. The last three columns are the number total, correct and incorrect statements in the development set. See Table 6 for more dataset statistics, and Table 7 for full citations and sources for these datasets.

---

Abbr.
Name
Statement Length
min median
90%
95%
99% max
STAGE A TRAINING
Atomic2020
Atomic2020
5
19
24
26
30
57
GenericsKB
GenericsKB
13
22
24
28
STAGE
(SEEN)
OBQA
OpenBookQA
5
16
29
36
56
74
ARC e
ARC (casy)
6
24
50
60
86
111
ARC_
ARC (hard)
30
59
70
94
138
A12Sci
Science (elem)
63
79
473
A12Sci
Science (middle)
58
536
CSQA
CommonsenseQA
28
QASC
QASC
19
24
PIQA
Physical IQA
62
80
120
256
SIQA
Social IQA
10
28
38
41
51
70
WG
Winogrande
17
24
31
34
38
42
C2S
Com2Sense (paired)
12
24
34
38
44
55
SciQ
SciQ
6
19
29
34
48
75
QuaRel
QuaRel
15
39
62
80
101
107
QuaRTz
QuaRTz
44
73
CycIC
CycIC (mc)
59
97
122
Com
ComVE (task
14
20
CSQA2
C 2.0
24
38
SKD
SKD (annotated)
25
31
12D2_anno
12D2 (annotated)
5
15
21
24
31
41
EVALUATION (UNSEEN TYPE 1)
WSC
WSC
45
COPA
COPA
10
21
23
26
28
NumerSense
NumerSense
6
13
20
23
32
36
PROST
PROST
17
42
63
68
78
78
SpatialCS
Spatial Commonsense
9
12
17
18
19
20
Rainier_ anno
Rainier (annotated)
5
12
19
21
29
33
EVALUATION (UNSERN
2)
SWAG
SWAG
12
30
46
52
67
148
HellaSwag
HellaSwag
15
103
134
140
149
181
CODAH
CODAH
5
31
34
45
73
SCT
Story Cloze Test
29
69
72
78
89
oNLI
oNLI
17
44
53
65
StrategyQA
StrategyQA
20
25
CREAK
CREAK
Table More datasct statistics. This table shows the percentiles statement lengths (as in number of T5 tokens) in each dataset.

---

Abbr:
Name
Citation
Link
In Flan-T5?
Atomic2020
Alumic2020
Hwang (2020)
https://allenai wwt.://////tttto..eeeoeee/
yes
GenericsKB
GenericsKB
Bbakthavatsalam al. (2020)
https://allenai. http:///ww...eeeeet////eeeerroee
STAGE B TRAINING (SEEN)
OBQA
OpenBookQA
Mihaylov al. (2018)
https://gi wttb.chhoooooooo.. wwl.eaaaaa//iidaaa
ARC_
ARC (easy)
Clark et al. (2018)
https: thu. h////tttubccccm/lleaaaaaa wwwaaaleena/uniiddaaeal funifiedga
yes
ARC_h
ARC (hard)
Clark (2018)
https: //g1 thgb, comm/alenai/unniiieee
yes
A12Sci_e
A12 Science (elem)
Clark et al. (2018)
https://gi thub. con/ http//aaleaa//niffiee
A12Sci
(middle)
(2018)
https://gi httpab/.ccmmallmmm/uiiiieoooo
CSQA
CommonsensseQA
Talmor et (2019)
https://gi cwn/ com/alleeaii/niiiieeee..
QASC
QASC
et (2019)
https://gith: wwt.:ooo/aaaaalaeeeeee
yes
PIQA
Physical IQA
Bisk al. (2019)
https://gi http:/hhh...acalmaaaaaaaaaa wttp:///leaaiamiiiiiiiioooooo
SIQA
Social IQA
Sap et al. (2019)
https://gi http:///gtthubb.cmalleeeeeee
cotp://acccmaallaaa/a///////// (ieda
yсs
WG
Winogrande
Sakaguchi
https://github. httpb//////aaa///mmm//aa f'eed
yes
C28
Com2Sense (paired)
Singh et al. (2021)
https://github.
master/data
SciQ
SeiQ
Welbi et (2017)
QuaRel
QuaRel
Tafjord et al. (2018)
yes
QuaRT>
QuaRT>
Tufjord (2019)
https: httpss//alleeaii..r http:s////llaaiioggiatttaaa http:/aai..og//taauuuurrrra
CycIC
CycIC (mc)
https://leaderbbardd.lll allanai org/cycic/
no
submisssions/get-strrr
ComVE
ComVE (task A)
Wang et al. (2020)
https://gittubb.cmo//aaa
CSQA2
CommonsenseeQA 2.0
Talmor (2021)
https://github... http:///gttttc.ccmmmallll/////pp dataset
SKD_amd
SKD (annotated)
West (2021)
https: h com/peterwestaii/
puri purification_cs
I2D2_ammo
12D2 (anmotated)
Bhagavatula et al. (2022)
www.salllaaanoooo nttps://genger www.ggen.pps..llleaaa.....
EVALUATION (UNSEEN TYPE 1)
WSC
WSC
Levesque al. (2011)
https://huggingare wateee.c//aaaaaa httpaaac...cdtttaaeeeeeeeeeeeeee co.datasetss/vingrraaaaaaa
COPA
COPA
Gordon et al. (2011)
https://huggingfaee
yes
NumerSense
NumerSense
Lin (2020)
https://github. Ltw./KKUS///eeeeeeeeeeeeeeeeeee data
yes
Aroca Quellette al. (2021)
https: https://huggingfacce wttp:////.aaatttessoooss.i....
yes
SpatialCS
Sputial Commonsemsee
Liu et ul. (2022c)
https://gi thub. thub, con/ wwx.ioiaaaaaaaaaooaooooa http://aaaallcooaaaaaeaaeaeeaaae commeesseeeeeeeeeeeeeeeeeeee
Rainier_anao
Rainier (onnotated)
et al. (20226)
https://gi http:ttwbcommc//cccc///eeeee
80
EVALUATION (UNSEEN TYPE 2)
SWAG
SWAG
Zellers et al. (2018)
yes
data
HellaSwag
HellaSwag
Zellers et al. (2019)
//gith// htt..///waae//eelllae//////eeeee
data
CODAH
CODAH
Chen al. (2019)
https://gi con/ www./eesswll-eeeeeeeeeeeo httphhhcc./eeeeesiii-////AAA///e
data
SCT
Story Cloze Test oNLI
Mostafazadeh al. (2016)
https://cs http://c.rrrcesttreeeee eddu/hlp/rocstorress//
oNLI
Bhugavatula et (2019)
allmmai /tt.mma...g//mm//ui/isiiiiiiooo
yes
get-started
Qu
StrategyQA
Geva et al. (2021)
tttps:///gthub. http:://gggtbbbcco//eeaaegg/s/ts cow//e/adeegllsssttteeyrreeee
main/data/str sttattagaa/ttaayyaaaaaa
CREAK
CREAK
Onoe al. (2021)
h/tis://gitt thub.
con/ thut
data/creak
Table More dataset details. We show the link from which we retrieved each dataset, and whether each dataset is included in the training data of Flan-T5.
Symbol
Value
Description
L
128
Max number of tokens the input statement Number of statement groups per batch
BG
64
C
Max number of statements in each group, during training
Bg
256
Max number of statements per batch. during training
50,000
Total number training steps each stage
171s
x 10
Learning rate for VERA with T5 encoder backbone
JILLMA
2 x 10
Learning rate for VERA with LLaMA backbone
1.0
Weight of binary classification loss Weight of multiple-choice loss
9
1.0
7
0.1
Weight of supervised contrastive loss
0.05
Temperature in supervised contrastive loss
Table 8: Hyperparameter settings.

---

random
Unified0W-22 (118)
ChatGPT
Vere-LLaMA (78))
SKD Citic (EGSM)
Entailer (118)
GPT4
VeraiTS (5B)
Cribic
(1758)
10en
unseen Rtppe
urseen (type 2)
0.5
Accurccy
0.7
0.7
01.0
0.5
0.5
0.5
0.4
0.4
Distrbutton of Predictios
The
True
0350
0903
5200
3903
Count
4000
2000
1000
-20
-3
0
-10
-3
10
-10
Logit
Logit 12)
0.3
Thae hosttve Tabe
Curve
0.2
02
0.2
0.3
02
0.4
56
3.5
3.5
02
24
04
0.5
1.0
3.0
022
D4
0.E
05
1.0
1050
1.0
2.0
64
0.6
0.0
0.0
0.0
Rocal
Rocal
Recal
0.3
1.6
0.2
02
02
0.3
02
0.
56
23
1.4
0.0
02
34
64
0.8
10
3.0
02
84
0.
06
1.0
Desal
2.0
1.0
1.0
frazzenn of Curret: Prediction
Predkctions
3.8
0.4
0.6
Calibration
0.4
0.4
0.44
009
022
0101
507
0.2
0.04
0.05
0.5
22
0.4
54
08 14
0.0
03
04
5A
1.0
5.0
07
2/1
06
68
Scare 00
Scom
Predicted
Figure Results problem-solving with VERA development sets are reported. Accuracy across different parts (seen, unseen (type 1), unseen (type 2)) are not directly comparable different underlying benchmarks. For calibration curves, curves saturated colors are results after applying post hoc calibration ($3.3), while curves with faded colors results from the raw logits.
and unseen benchmarks. Average results the

---

OBQA
ARC_,
ARC_h
A
Al2Sci
CSQA
QASC
MIQA
SIQA
SciQ
QuaRell
QuiRT:
Cyc3C
ComVE CSQA2
SKD Cliic (355M)
36.64
35.06
47.60
27.60
29.12
23.18
25.20
28.00
20.15
12.42
53.86
30
50.28
$1,41
27.50
53.60
55.73
25,80
52.56
47.60
1202 Crite (35M))
55.03
55.75 43665
44.80
55.61
35.79
55.28
50,40
61:51
45.25
67.36
56.45
55.
56
63.17
55.10
61.51
60.16
35.72
88.26
43.65
Unitee0QA-x2 (1IB)
56.34 56.05
54,60
48.77
39,46
48.78
43.20
32.61
63.9%
52.00
70.64
25.96
42.20
81,65
48.29
83.65
56.05
Emailer (LIB)
73.79 74,90 56.00
71.40
$1.93
61.88
77.24
$2.40
67.81
57.56
78.76
61
77.11
82.86
76.90
85.97
76.56
52.81
96.89
$6.00
PPL 3.)))355
66.02 66.02
45.20
70.12
45.15
69.92
62.70
61.92
57.02
91.55
51
71.57
12.56
86.80
71.22
70.91
53.31
$5.37
GPT-3.5 (1758)
75.41 76.34 60.55
74.20
83.79
68.90
$4.55
$0.80
66.91
62.85
84.17
65 30
72.53
81.33
86.00
83.09
76.04
$1.50
97.39
60.55
ChoIGP
62.11 61.52 71,45
61.80
65.44
57,19
63.41
66.00
39.64
42.01
67.36
52 20
61.33
76.73
60.70
74.10
72.66
29.66
92.08
71.65
CoT
62.40
77.33
72.36
82.14
69.77
12.56
GPT-4
72.35 71.81 81.00
76,00
09.00
72,00
80,00
90,00
43.00
44,00
13.00
57.00
77.00
94,00
70.00
86,00
30.00
53,00
95.00
81.00
+S CoT
74.96 74,96
79.80
78.00
70,00
87,00
89,1
31,31
44,00
80.00
00
82.18
91. 92
80.00
82,00
87.00
57,00
92,08
Plan-T5 (118)
79.50 80158
79.60
85.79
71.24
86.99
81.60
69.21
64.58
83.95
84.
M0.80
92.81
69.30
98.41
62:25
VERA- LLaMA (7B)
82.99 84.J8
63.85
$0,20
$4.39
75.92
58.62
$2.40
76.17
71.38
85.91
79.89
$7.92
83.633
90.00
92.09
30,99
89.12
97.99
63.85
VERA-15 (5B)
85.51 86.57 68.00 83.20
88.07
78.60
93.50
86.40
77.90
73.33
88.47
80.14
92.42
85,93
88.80
93.88
$4.90
91.73
91.79
68.60
Table 9: Results on seen benchmarks. Accuracy on the development set is reported.
Dataset
All
MC
Bool
WSC COPA NumerSense PROST
SpatialCS
SKD Critic (355M)
38.34
35.83
48.41
54.21 53.00
11.50
24.60
48.41
12D2 Critic (355M)
54.79 54.43
56.22
80.59
72.80
35.00
29.35
$6.22
UnifiedQA-v2 (1IB)
59.73 55.10
78.25
71.79 81.20
35.00
32.40
78.25
Entailer (IIB)
71.47 68.05
85.15
86.08 92.40
51.00
42.70
85.15
G.T-3.5 (175B)
71.03 70.73
72.24
85.71 87.00
66.50
43.70
72.24
ChaiGPT
61.20 $4.69 87.22
73.26 58.80
47.50
39.20
87.22
GPT-4
77.40 71.75 100.00
85.00 64.00
69.00
69.00
100.00
Flan-T5 (11B)
77.62
73.22
95.23
90.48 93.00
57.50
51.90
95.23
VERA-LLaMA (7B)
75.71
74.06 82.32
94.14 91.80
65.00
45.30
82.32
VERA-T5 (5B)
81.65 78.70 93.44 94.51 93.40
66.50
60.40
93.44
Table 10: Results on unseen (type benchmarks. Accuracy on the development set is reported.
Dataset +
All
MC
Bool SWAG HellaSwag CODAH SCT oNLI StrategyQA CREAK
SKD Critic (355M)
43.40
40.11 51.62
26.95
30.45
29.35
62.75 51.04
50.66
52.59
12D2 Critic (355M)
67.11 70.42 58.84
72.15
53.30
67.30
88.24
71.08
52.40
65,28
Uni (IIBB
53.95 52.83 56.77
31.75
36.60
49.00
82.04 64.75
49.34
64.19
Entailer (1IB)
70.72 70.63 70.94
52.45
47.65
80.70
94.39 77.94
60.26
81.62
GPT-3.5 (175B)
78.87 80.21 75.53
73.40
70.40
85.05
95.56 76.63
62.88
88,18
ChatGPT
62.83 56.21 79.39
43.70
42.95
56.75
77.34 60.31
67.69
91.10
GPT-4
70.29 66.20 80.50
57.00
40.00
66.00
93.00 75.00
70.00
91.00
Flan-T5 (11B)
78.89
80.52
74.81
69.20
64.55
89.60
98.45 80.81
61.14
88.48
VERA-LLAMA (7B)
82.56 86.11 1 73.71
79.30
83,90
88.95
98.61
79.77
62.45
84,97
VERA-TS (5B)
83.37 86.66
75.13
76.30
85.90
88.60
98.56
83.94
65.07
85.19
Table 11: Results on unseen (type 2) benchmarks. Accuracy on the development set is reported.
Generator
Filter QA
Avg OBQA ARC_e ARC_h Al2Sci_e A12Sci_m CSQA
QASC
PIQA
SIQA WG
UnireedQA-large 60.45
70.20
69.12
55.85
69.11
64.80
61.43
43.09
63.66 53.84 53.35
GPT-3 (davinci)
UnifiedQA-lame 67.44
74.60
75.44
64.55
69.92
72,80
70.19
63.82
67.74 58.70 56.59
GPT-3 (davinci) VERA UnifiedQA-large 70.67 77.60
80.00
67.56
78.05
78.40
71.91
66.20
70.35
$9.37 $7.22
Generator Filter QA
Avg OBQA ARC_e ARC_h h Al2Sci_e A12Sci_m CSQA
QASC PIQA SIQA
WG
Unified@AA-arge 60.45
70.20
69.12
55.85
69.11
64.80
61.43
43.09
63.66
53.84 53.35
Rainier-large
UnifiedQA-large 61.78
69.40
66.84
52.84
68.29
$7.60
68.30
54.86
65.51 56,81 57.38
Rainier-large VERA UnifiedQA-laree 64.88
73.40
71.05
57.19
73.98
67.20
68.30
55.51 67.52 56.96 57.70
Table 12: Results of introducing VERA into the Generated Knowledge Prompting pipeline (Liu al., 2021). Accuracy on the development set is reported.

---

random
UnifedOA-v2 (115)
ChatGPT
Vere-LLaMA (76)
Critic (355M)
GPT4
Vera-75
12D2 CAiti: (355M)
GPT:3.5 (1758)
Flan-TS (1118)
SKD anno (seen)
I2D2_anm (seen)
Rainier
aannieraannoooo anno (unseen)
Distribution of Predictions
False
160
Ralse
False
100
Count
80
100
Count
40
40
20
10
-5.0
-2.5
2.5
-10
-10
Predicted
Predicted
Predicted
0.8
0.88
0.8
True Positive Rate
Positive
0.0
0.0
Positive
0.0
ROC
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0.8
2.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
1.0
Faloe Positive Rate
Felse Positive Rate
Feloe Positive Rate
1.0
P.R Curve (True)
Precison
0.6
0.6
Recall
Recall
1.0
3.0
1.0
0.8
0.8
0.8
P.R Curve (False)
Predttion
0.6
0.6
Predition
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Recoll
Recall
Recail
1.0
1.0
2.0
Fraction Correct Predctions
Froction of Corract Predctions
Fraction Correct Predictions
Calibration Curve
0.8
0.8
0.6
0.6
0.6
ECE (4)
ECF (L)
FCE (1)
0.4
0.00
0.00
0.2
0.03
0.2
0.01
0.2
0.32
0.04
0.07
0.17
0.08
0.6 Predicted Score (5)
Predicted Score (5)
Predicted Score (5)
Figure 6: Results for filtering LM-generated commonsense knowledge with VERA. Results on the development sets are reported.

---

0.8
Accuracy
0.7
verification
0.5
UnifiedQA-v2
GPT-3.5
ChatGPT
Flan-T5
Vera
Figure 7: Comparing verification and QA, two different formats for prohlen--olvving tasks. Average accuracy on the development sets of the multiple-choice benchmarks is reported. We text-davinci-002 GPT-3.55 here, and gpt-3. t-tuubbo0000 as ChatGPT. VERA in QA format actually means T5 model finetuned on the same seen multiple-choice data as VERA.


Reviews:
[{"review_id": "uFtxyaeypw", "reviewer": "Reviewer_FtwG", "summary": "", "questions": "N/A", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the insightful feedback! We appreciate your recognition of the soundness of our proposed method and the coverage of our experiments.\n\nBelow are our responses, in which we try to clear up some misunderstandings:\n1. **Technical novelty:** We show that adopting certain techniques (e.g., contrastive objectives, two-staged training, negative data augmentation) are critical in producing a strong commonsense discrimination model. This is supported by another reviewer who said “the combination of training objectives and the dataset preprocessing is novel.” Also please note that “the method is too simple” is one of the [deprecated heuristics](https://2023.aclweb.org/blog/review-acl23/#2-check-for-lazy-thinking) in the reviewing guidelines from ACL.\n    * Additionally, instead of focusing on intricate technical novelty, our main contribution consists of task formulation and open resources. We observe that the latest LMs fall short in generating correct commonsense statements and detecting incorrect ones. We formalize the task of commonsense plausibility estimation, gather diverse data sources and process them into declarative format, and present a strong commonsense model. All code, data and models will be made publicly available, and we believe they will be useful resources to the NLP research community.\n2. **Calibration on baselines:** Our calibration is a post-hoc strategy and does not affect the task performance metrics we report in Sec 5, so *our experimental results and comparisons are valid and unbiased*. We briefly mentioned this in Sec 3.3 (Lines 347-350), and below we provide a detailed justification (which we will add to our next paper update):\n    * Applying our calibration method – temperature scaling – does not affect the relative order of plausibility scores assigned to a given set of statements.\n    * For tasks with multiple-choice questions (Sec 5.1), calibration does not affect the argmax prediction for the above reason.\n    * For commonsense knowledge filtering (Sec 5.2), calibration does not affect the TPR/FPR numbers at each corresponding decision point, again for the above reason, so the ROC curves are valid.\n    * For True/False judgment problems (Sec 5.1 and 5.3), calibration does not move the plausibility scores across the decision boundary. We use logit z=0.0 (or equivalently, plausibility score s=0.5) as the True/False boundary. A positive (or negative) logit remains positive (or negative) after applying the temperature.\n3. **Ablation of calibration:** We reported the impact of calibration in Figure 2 and Appendix Figure 3. Under “Calibration Curve”, we plot the calibration curve for both the uncalibrated version (w/ faded color) and calibrated version (w/ saturated color) of the Vera model. As shown in Figure 3, applying calibration reduces the calibration error (ECE) from 7% down to 1% on seen datasets, and from 9% down to 2% on unseen datasets. We will add more discussion on the impact of calibration in our next paper update.\n4. **ChatGPT/GPT-4 CoT baselines:** Thanks for the suggestion! We are adding the ChatGPT/GPT4 baseline with few-shot CoT, and report their problem-solving performance on seen benchmarks as below (and contrast with zero-shot ChatGPT/GPT-4 as well as Vera):\n| Model | Seen |\n| --- | --- |\n| ChatGPT (few-shot CoT) | 65.19 |\n| GPT-4 (few-shot CoT) | 74.96 |\n| ChatGPT (zero-shot) | 62.11 |\n| GPT-4 (zero-shot) | 72.35 |\n| Vera-T5 | **85.51** |\n\n    * We found that adding few-shot CoT increases the performance of ChatGPT/GPT-4 by about 3%, and they are still below Vera by a large margin. We will replace the zero-shot ChatGPT/GPT-4 baselines with this few-shot CoT version in our next paper update.\n    * The few-shot CoT template we use (for OpenAI ChatCompletion API) is: \n```\nmessages=[\n    # below is one demonstration. The statements and explanations are dataset-specific\n    {'role': 'user', 'content': 'Question: Based on commonsense knowledge, is the following statement correct? Please first generate an explanation and then answer yes or no.\\nStatement: Beads is formed by clouds.'},\n    {'role': 'assistant', 'content': 'Beads of water are ... So the answer is yes.'},\n    # 4 more demonstrations, some with no as answer\n    ...\n    # below is the evaluated commonsense statement\n    {'role': 'user', 'content': f'Question: Based on commonsense knowledge, is the following statement correct? Please first generate an explanation and then answer yes or no.\\nStatement: {statement}'},\n    # and we extract the second-last token (yes or no) from the next assistant response\n],\n```\n5. **Ablation on training objectives:** We discuss the impact of training objectives in Appendix E.1, and experimental results can be found in Figure 5. We will add these results to the main text in our next paper update.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper is overall well written and easy to follow.\n2. The proposed method is technically sound and clearly explained/formulated.\n3. Empirical results are promising: fine-tuning a T5 can outperform ChatGPT and GPT-4 is impressive. The authors also explore some applications and conduct some analysis for the proposed method.\n4. The authors conduct experiments on many datasets/settings and many results are provided.", "reasons_to_reject": "1. The idea of training a discriminative model to verify predictions from other models is not new. While the application on commonsense statement verification is new and meaningful, the technical novelty of the method is not very strong.\n2. The experimental comparisons are not very solid because the calibration methods are only applied to Vera but not to other models, which can lead to a very biased comparison, especially no ablation study on the impact of calibration is done. Second, the LLM baselines including ChatGPT and GPT-4 are not very carefully engineered (no step by step reasoning of in-context learning is done). There also lacks some analysis on the impact of the proposed combination of training objectives.", "questions_to_authors": "N/A", "paper_topic_and_main_contributions": "This paper proposes training a discrimination model called Vera for commonsense statements. The authors pre-train Vera on both commonsense question answering datasets and commonsense knowledge bases. The authors also propose to incorporate a combination of three objectives to train the model. After training, the authors show that when combined with some calibration techniques, Vera outperforms existing models that can be used for commonsense statement verification and can be used for filtering machine-generated commonsense knowledge.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Language Models (Mostly) Know What They Know Kadavath et al. 2022"}, "extra_text": {}}, {"review_id": "eALJJvdUCA", "reviewer": "Reviewer_HcPV", "summary": "", "questions": "I think it was overall an excellent work. I think the scaling of the models should be put in the main body of the work later and can help inform people. With a t5-large you can decent performance in a considerably smaller model. \n\nOne question is will the code for pre-processing be released? ", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your valuable feedback and suggested additional references! We really appreciate your recognition of the diversity and coverage of our evaluation, and the values in our analyses and baselines.\n\nBelow is our response to your concerns: (we will include the additional experimental results in the next paper update)\n1. **Cross-entropy baseline:** Thanks for your suggestion! We experimented with this cross-entropy baseline, with GPT-3.5 (text-davinci-002), and below is the comparison with other methods, on the problem-solving evaluation:\n| Model | Seen |\n| --- | --- |\n| Cross-entropy (text-davinci-002) | 66.02 |\n| text-davinci-003 | 75.41 |\n| Vera-T5 | **85.51** |\n    * This cross-entropy method greatly underperforms the zero-shot version of GPT-3.5 as well as Vera.\n2. **Simplicity of modeling:** Our model architecture is indeed simple and standard in classification setup. In early experiments we found it unhelpful to increase complexity (e.g., replacing the classifier head with MLP), so we favored simplicity for easier model distribution with HuggingFace. Despite the simplicity, our model is a strong, one-of-a-kind model for commonsense, and we believe it can be a useful resource for the NLP research community.\n    * We appreciate your recognition of novelty in our method.\n3. **Contextualized commonsense:** We agree that commonsense statements are often contextualized, and that contextualized/defeasible commonsense inference is an important research problem. In this work, we focused on classifying *self-contained commonsense statements* (Sec 2, Line 120), because it is a problem with its own significance and challenge. We will discuss contextualized commonsense inference as future work in our next paper update, along with existing discussion of multi-sentence commonsense statements (Sec 7, Line 627).\n4. **Table 3 symbols:** We measure “usefulness” as the increment in accuracy due to contextualizing on the commonsense knowledge, and the delta is the relative change in knowledge usefulness when we apply Vera filtering.\n5. **Ablation on training objectives:** While eliminating multi-class loss increases performance by ~2% on boolean benchmarks in unseen (type 1) set, it decreases performance by ~1% on multiple-choice benchmarks in the unseen (type 1) set, and slightly harms performance on benchmarks in the seen set. As reported in Table 7, multiple-choice benchmarks have a majority in the unseen (type 1) set, so the overall performance is higher when including the multi-class loss.\n6. **Significance:** We did one run per experiment. The gains are significant due to the large margin and large evaluation set. We additionally conducted a paired t-test on the problem-solving performance (Sec 5.1) between Vera-T5 and the best baseline, Flan-T5, and demonstrate that our gains are indeed statistically significant:\n| Eval Part | Gain (%) | n | t-statistic | p-value |\n| --- | --- | --- | --- | --- |\n| seen | +6.01 | 14779 | -19.10 | 1x10^-80 |\n| unseen (type 1) | +4.03 | 4420 | -6.79 | 6x10^-12 |\n| unseen (type 2) | +4.48 | 11002 | -12.64 | 1x10^-36 |\n7. **Ablation on training stages:** Thanks for the great suggestion! We additionally experimented with your two suggested ablations: (1) mixing the data of Stages A and B and training in one single stage; (2) inverting the order of Stages A and B. These ablations underperform our best model. Below is their problem-solving accuracy, compared to the original Vera-T5:\n| Model | Seen | Unseen (type 1) |\n| --- | --- | --- |\n| Vera-T5 | **85.51** | **81.65** |\n| mixing | 84.17 | 81.33 |\n| invert | 84.78 | 80.18 |\n8. **Open-sourcing:** All code, data and models will be made publicly available, and we believe they will be useful resources to the NLP research community.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Some reasons to accept the work are the following:\n* Excellent writing, very clear and to the point presentation of the problem of lack of verification for commonsense statements\n* Wide variety of tests on a wider variety of datasets with different types of statements\n* Excellent appendix with useful information particularly on how effective smaller models are for this same task\n* The authors evaluate a variety of very large language models to test the verification of commonsense assertions which is good for the whole community \n", "reasons_to_reject": "Some suggestions:\n* The authors may want to include a simple baseline of a GPT-2 cross entropy of tokens. It is used in https://ojs.aaai.org/index.php/AAAI/article/view/17521 and I believe that it would be good to compare this type of score (thresholded possibly) with the scores of the model the authors propose.\n* The work, although exhaustive in tests, is very simple in nature: it consists of putting a classifier head on a language model to score sentences. Although the combination of training objectives and the dataset preprocessing is novel, the classification head on a language model is not.\n* The work does not touch on how context plays a part of commonsense facts. Contextual commonsense inference is an important problem (as commonsense information may shift through a narrative), and this work does not address this particular issue (it may be good to mention it for future work)\n* The delta percentages in table 3 should be removed and usefulness should be replaced with the delta symbol.\n* The argument that the multi-label classification improves performance is not very backed up by Figure 5. Additionally, the ablation studies should have some error margins and significance to see if they actually do anything. It seems elminating multi-class loss *improves* the accuracy in type 1 things so how are you arguing that it is useful?\n* No amount of runs are given, if significance is claimed (line 641) you may want to back up this statement with more runs/actual significance tests.\n* I could not find good backing up of why the 2 stage training is better than mixing the data or the inverse of the stages, please list this in the appendix (or make it clearer where this is)\n", "questions_to_authors": "I think it was overall an excellent work. I think the scaling of the models should be put in the main body of the work later and can help inform people. With a t5-large you can decent performance in a considerably smaller model. \n\nOne question is will the code for pre-processing be released? ", "paper_topic_and_main_contributions": "The paper proposes utilizing a language model, trained on a dataset of commonsense assertions and assertions adapted from QA datasets, to give a plausibility score for commonsense statements.  The author's show that their proposed method has high accuracy and recall on a wide variety of benchmarks. The methods are simple to implement (some pre-processing of data, and simple training with 3 different objectives).", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "I could not find typos, but I was not very active in looking for them. Overall excellent work!", "Justification_for_Ethical_Concerns": "n/a"}, "extra_text": {"Missing_References": "For future work Contextual commonsense inference:\nhttps://ojs.aaai.org/index.php/AAAI/article/view/17521\nAn approach to score contextual commonsense assertions:\nhttps://arxiv.org/pdf/2302.05406.pdf\nA source on commonsense plausibility:\nhttps://aclanthology.org/P16-1137.pdf\n"}}, {"review_id": "ntkpNvE4ZD", "reviewer": "Reviewer_HDaL", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the insightful feedback! We really appreciate your recognition of our work’s importance to the community, and our model’s superior performance over state-of-the-arts.\n\nDue to space limits, we had to put many details in the Appendix, but we will move them back to the main text in our next paper update, using the extra page. Below is our response to your specific concerns:\n1. **Ablation on training objectives:** We have already demonstrated the impact of training objectives in Appendix E.1, and experimental results can be found in Figure 5. We will move this into the main text in our next paper update.\n2. **Hyper-parameters:** We report the hyperparameters in Appendix C and Table 8, including the weights of each component in the combined training objectives. Specifically, we use 1.0 for the binary classification loss, 1.0 for the multiple-choice loss, and 0.1 for the supervised contrastive loss. These values are obtained from some moderate hyperparameter tuning, and we did not do extensive search due to training cost. We will make sure the details about hyperparameter tuning are clear in the paper, so as to promote reproducibility.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The VERA model for plausibility estimation is of significant importance to the NLP community. It serves as a versatile tool, capable of being employed as both a reward model and a classifier for intermediate steps in various NLP tasks.\n\n2. The paper's structure is well-organized, offering a clear and easy-to-follow presentation of the research content.\n\n3. VERA demonstrates superior performance compared to existing models in several tasks, even outperforming GPT-4 in certain scenarios.", "reasons_to_reject": "1. The main papers' content feels incomplete, and I believe that the ablation analysis to investigate the effect of each training objective holds crucial importance. It would be more beneficial to move this section to the main paper for better understanding and comprehensive insights.\n\n2. The training objectives appear somewhat complex, and the lack of details about hyper-parameters in the experiments is a concern. Combining three loss functions might prove challenging during training without explicit guidance on hyper-parameter settings.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper introduces VERA, a general-purpose plausibility estimation model for commonsense statements. The model is trained on a large-scale dataset consisting of correct and incorrect commonsense statements sourced from commonsense QA datasets and knowledge bases. VERA outperforms existing models in solving commonsense problems, filtering LM-generated commonsense knowledge, and detecting commonsense errors made by ChatGPT.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes a method, Vera, that estimates the plausibility of any given commonsense statement.  Vera is built by finetuning T5 on ~6M statements from two commonsense KBs and 19 commonsense QA datasets. The paper is well-written, and the author compares their method with several LLMs. VERA outputs a real-valued score in [0,1]. The idea of using Vera to detect errors made by ChatGPT is interesting. 

Pros: 

- The idea of automatically estimating the plausibility of a natural language statement with respect to commonsense knowledge is important and will be useful for the community. 
- As agreed by all reviewers, the paper has promising results, especially Vera outperforming ChatGPT and GPT-4 in estimating plausibility. 
- Vera is able to detect erroneous commonsense statements from generative LMs (ChatGPT).  

Cons: 
- The authors show that Vera as a filter or critic can be useful for reinforcement-based QA models (Table 3). However, the Area Chair thinks there can be a knowledge leakage since Vera is finetuned on these datasets. It will be more useful if the authors show it on the Unseen dataset. 
- The authors do not explain why a real-valued score is used to estimate the plausibility. For example, it is difficult to understand the difference between 0.6 and 0.65 plausibility scores.  It would be interesting to see how the plausibility score relates to human understanding of commonsense plausibility. 
- The error (quantitative) analysis is missing. An analysis with respect to different common sense knowledge dimensions (physical, social, etc.) would make the paper stronger. 

There are a few useful results and analyses in the Appendix of the paper. Especially the ablation study should be included in the paper. 

Finally, The AreaChair appreciate the authors' response to clarify the doubts of the reviewers.
Decision: Accept-Main