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A VITAMINC: Complementary details
We provide additional details about the VITAMINC dataset.
A.1 Claim Statistics
Topic Distribution. Figure A.1 shows the distribution of claims in the VITAMINC dataset by the topic of the Wikipedia article they are based on. The information was collected from DBpedia, $^{12}$  retrieving the parent class of the pages. Labels for about  $25\%$  of the articles were missing, and left blank in the diagram.
The "synthetic" part of VITAMINC, which is based on the claims of the FEVER dataset, contains many claims about specific human entities. About  $15\%$  of the claims in VITAMINC real are about COVID-19.
Category Distribution. We sample 100 examples from the "real" and "synthetic" subsets of VITAMINC and manually categorize their claims. The results are presented in Table A.1. Due to the creation methodology of VITAMINC real, its claims mostly describe frequently updating facts, or facts that tend to be corrected. We find about half of these claims to describe changes in numerical values (e.g., number of COVID-19 cases, earnings or ratings of movies, number of awards etc.). In contrast, VITAMINC synthetic mostly covers general facts about specific entities, (e.g., place of birth, date of birth, occupation, etc.). This is a result of the synthetic claims being based on the FEVER dataset, where annotators were asked to come up with claims on popular Wikipedia pages. Combined, the VITAMINC dataset holds a diverse set of claims about various topics.
Figure A.1: Distribution of claims in the VITAMINC dataset by the topic of the originated Wikipedia article.
A.2 Inter-annotator Agreement
We ask three additional annotators to independently annotate a random set of two thousand claim-evidence pairs, evenly distributed between the development and test splits of the real and synthetic sets. The Fleiss  $\kappa$  score (Fleiss, 1971) between the four annotations is 0.7065, which means substantial agreement. Similar agreement scores of 0.6841 and 0.7 were reported for fact verification (Thorne et al., 2018) and NLI datasets (Bowman et al., 2015), respectively.
A.3 Claim-only Classification
Annotation artifacts are common in crowd-sourced sentence-pair inference datasets such as fact verification and NLI. Models can leverage these idiosyncrasies to achieve unexpectedly high performance when given only one sentence of the pair. For example, Schuster et al. (2019) showed that a claim-only classifier can obtain  $61.7\%$  accuracy. The VITAM-INC dataset avoids this bias by pairing each claim with two contrastive contexts.
$^{12}$ http://dbpedia.org/ontology/
Claim category	real	synthetic	Example
Quantitative	48%	9%	The COVID-19 pathogen may last less than 10 days on some surfaces.
Calendrical	9%	15%	Italy surpassed the 10,000 coronavirus-related deaths on a Saturday.
Entity	23%	58%	Mary of Teck was queen-consort.
Event	14%	14%	In the last EFL Cup, Manchester defeated Chelsea.
Other	6%	4%	Most genes need further research to better understand the function of their RNA products.
Table A.1: Estimated distribution of claims in the VITAMINC, based on manual annotations of 100 randomly sampled claims from the development split of the real and synthetic subsets. An example claim from each category is provided for reference.
(a) VITAMINC real
(b) VITAMINC synthetic
Figure A.2: Probability density function of claim-evidence overlap for different labels in the dataset. The overlap is computed as the ratio of mutual bigrams in the two sentences.
(c) FEVER
All claims in the VITAMINC-synthetic are paired with one refuting and one supporting evidence, making it impossible for a claim-only to perform better than random. Each claim in the VITAMINC-real is paired with one refuting or neutral evidence, in addition to a supporting one. To evaluate whether models can utilize lexical cues in claims, we train a claim-only classifier on VITAMINC-real and find it to achieve  $50\%$  accuracy—the same as always predicting SUP.
A.4 Claim-evidence Word Overlap
Naturally, when pairing claims to evidence sentences, the overlapping words will be higher on average for claims with their supporting evidence. In VITAMINC dataset, we want to minimize this bias in order to create challenging examples that require sentence-pair inference and cannot be solved by simple word matching techniques. Therefore, we asked annotators, when possible, to avoid copying exact phrases from the evidence to the claim (see §3.2).
Figure A.2 shows the probability density function of bigram overlaps between the claim and evidence for each relation. Similar to FEVER, the overlap ratio of supporting pairs in the VITAMINC dataset is only slightly higher than the one of refuting pairs. Also, the overlap ratio of the NEI pairs of the VITAMINC real dataset is on average higher than FEVER.
We implement all our models with the HuggingFace Transformers library (Wolf et al., 2019). When comparing across training datasets of different sizes, we train the model for the same amount of update steps, upsampling the smaller datasets. We pick the checkpoint with the highest accuracy on the development set of the training task and report performance on the test set. More details are available at https://github.com/TalSchuster/VitaminC
C GPT-3 Evaluation
The GPT-3 model has recently demonstrated impressive results in zero-shot and few-shot generation and classification tasks (Brown et al., 2020). This 175B parameters language model was trained on billions of words from online sources, including the English Wikipedia. As a result, it can be applied on many tasks without any further fine-tuning—instead, one need only provide a task-specific prefix (i.e., "prompt") with a few examples that directly relate the language model towards the desired output format. For example, GPT-3 achieves better than random results on ANLI with only a single example in the prompt, and over  $40\%$  accuracy with 50 examples (Brown et al., 2020).
We used OpenAI's beta API to query GPT-3. Due to our limited quota, we could not perform
extensive experiments. Instead, we performed a qualitative evaluation using several examples from VITAMINC test set for the claim extraction (factually consistent generation) and the fact verification tasks. Therefore, these results should be viewed as exploratory only.
GPT-3 for Claim Extraction. We examine a two-shot setting for the claim extraction task. The model is asked to convert a revision into a short claim that expresses the fact that is true after the edit. To guide the model for this task, we provide a prompt with two random examples from the VITAMINC training set (see Figure C.1). One of the main concerns regarding large language models is the limited control it allows for ensuring that the facts in the generated output align with the source (Schuster et al., 2020). The generation tasks of VITAMINC provide a useful test-bed for evaluating the factual consistency with the input. Importantly, our VITAMINC-trained fact verification classifiers  $(f_{\text{verdict}})$  allow strong automatic evaluation for the factual agreement of the generation with the source.
We use GPT-3 to extract claims for four revisions with a sampling temperature value  $(\mathcal{T})$  set to either 0 or 0.7. The zero value is recommended for maximizing the factual consistency as the model follows its most certain predictions. Using low temperature, however, can result in less fluent generations (Holtzman et al., 2020). Therefore, high values of  $\mathcal{T}$  are also commonly used.
The results are reported in Tables 7 and E.3. With only two guiding examples, GPT-3 is able to follow the desired format and create a short claim. Yet, some of its generations follow  $s_{t-1}$  instead of  $s_t$  or add new, unsupported facts.  $f_{\text{verdict}}$  provides an indication for the factual correctness of the output. For example, it correctly classifies the output of the  $\mathcal{T} = 0.7$  setting for the top example in Table 7 as "Not Enough Information" since GPT-3 reported about 20 deaths even though the input doesn't mention death numbers at all.