# Investigating Zero- and Few-shot Generalization in Fact Verification

Liangming Pan$\dagger$
University of California, Santa Barbara
<EMAIL_ADDRESS>Yunxiang Zhang$\dagger$
University of Michigan, Ann Arbor
<EMAIL_ADDRESS>Min-Yen Kan
National University of Singapore
<EMAIL_ADDRESS>

###### Abstract

We explore zero- and few-shot generalization for fact verification (FV), which
aims to generalize the FV model trained on well-resourced domains (e.g.,
Wikipedia) to low-resourced domains that lack human annotations. To this end,
we first construct a benchmark dataset collection which contains 11 FV
datasets representing 6 domains. We conduct an empirical analysis of
generalization across these FV datasets, finding that current models
generalize poorly. Our analysis reveals that several factors affect
generalization, including dataset size, length of evidence, and the type of
claims. Finally, we show that two directions of work improve generalization:
1) incorporating domain knowledge via pretraining on specialized domains, and
2) automatically generating training data via claim generation.

††footnotetext: $\dagger$Equal Contribution.

##  1 Introduction

With a rise in deliberate disinformation, Fact Verification (FV) has become an
important NLP application. FV aims to verify given claims with the evidence
retrieved from plain text. Rapid progress has been made by training large
neural models Zhou et al. (2019); Liu et al. (2020); Zhong et al. (2020) on
the FEVER dataset Thorne et al. (2018), containing more than 100K human-
crafted (evidence, claim) pairs based on Wikipedia. Fact verification is also
needed in other domains, including news, social media, and scientific
documents. This has spurred the creation of a large number of FV datasets,
such as COVID-Fact Saakyan et al. (2021), SciFact Wadden et al. (2020), and
Climate-FEVER Diggelmann et al. (2020).

However, considering that human annotation is time-consuming, costly, and
often biased, it is difficult to collect reliable human-labeled data in every
domain that demands fact verification. We need to investigate how to build a
generalizable fact verification system that adapts to new domains with zero or
few samples. Critically, how can we leverage valuable (evidence, claim, label)
annotations from rich-resourced domains (e.g., Wikipedia) to aid fact
verification in the low-resourced ones (e.g., scholarly documents, and social
media)? Although FV datasets have been recently created in different domains,
little analysis has shown whether FV models generalize across them and to what
extent existing datasets can be leveraged to improve performance in these new
domains.

In this paper, we bridge this gap by conducting a comprehensive investigation
of zero- and few-shot generalization in fact verification. By conducting a
holistic study of FV datasets to date, we first carefully select 8 datasets
that have artificial or natural claims, human-annotated evidence, and two or
three-class labels for our study. We then standardize their data formats as
(evidence, claim, label) pairs and create dataset variants with different
granularity of evidence, which gives us a total of 11 datasets. We then
conduct a thorough empirical study of generalization and transfer across these
11 datasets. We train models on a source dataset, and then evaluate their
performance on a target dataset, either without any additional target training
examples (zero-shot setting) or with a few additional target examples (few-
shot setting).

We find that RoBERTa-based FV models tend to overfit the particular training
set, generalizing poorly to other datasets. Our in-depth analysis shows
generalization is related to several key factors, including dataset size,
length of evidence, and the claim type. In particular, we find that Wikipedia-
based artificial claims (e.g., FEVER) generalize well to natural claims in
real-world domains with the growth of dataset size, in contrast to prior work
that criticized crowd-sourced claims as having strong annotation bias and
being unrepresentative of real-world misinformation Schuster et al. (2019).
Our few-shot generalization experiment further shows that fine-tuning on a
small amount of target training data can substantially improve performance.

Armed with the above insights, we explore two ways to improve the
generalization of fact verification models. 1) Domain-specific Pretraining:
initializing the FV model with language models pretrained on specialized
domains, and 2) Data Augmentation: automatically generating training data for
the target domain. Results show that these methods can noticeably improve
generalization but still leave unsolved challenges such as inflexibility, high
cost, and label consistency.

To the best of our knowledge, this is the first work to perform a thorough
investigation of generalization and transfer in fact verification. We open-
sourced our dataset collection and codes to support future research towards a
universal and robust fact verification
system111https://github.com/teacherpeterpan/Fact-Checking-Generalization.

##  2 Dataset Curation

In this section, we describe the 11 fact verification datasets included in our
study. We first describe the criteria for dataset selection (§ 2.1), and then
we introduce the dataset processing (§ 2.2). We show the key characteristics
of the datasets in Table 1.

###  2.1 Dataset Selection

A large number of datasets have recently been introduced to study various
tasks for fact-checking, e.g., claim detection, evidence retrieval, fact
verification, justification production, etc. Our focus, fact verification, in
particular, takes a textual claim and a piece of evidence as input to predict
the label for the claim. Let’s define these aspects:

$\bullet$ Claim: Claims for fact verification are often textual, sentence-
level statements, which are categorized into: 1) real-world natural claims
crawled from dedicated websites, textbooks, forums, etc. 2) artificial claims
written by crowd-workers.

$\bullet$ Evidence: Evidence is the relevant information source for validating
the claim. Textual sources, such as news articles, academic papers, and
Wikipedia documents, are one of the most commonly used types of evidence.
Based on the granularity, we categorize the evidence in existing datasets
into: 1) document-level evidence such as the Wikipedia page Thorne et al.
(2018), news articles Hanselowski et al. (2019), and scientific papers Wadden
et al. (2020). 2) sentence-level evidence annotated by human experts in the
relevant documents to support or refute each claim. 3) no evidence is given
for each claim; the model needs to retrieve evidence from a large knowledge
source.

$\bullet$ Label: The label definition for the claim also varies across
datasets. The most common definition is the binary label with supports and
refutes, and the three-class label, i.e., supports/refutes/not enough info.
Some works Wang (2017); Augenstein et al. (2019) also employ multi-class
labels for more fine-grained degrees of truthfulness (e.g. true, mostly-true,
mixture, etc), where the number of labels vary greatly, ranging from 4 to 27.

Dataset | Domain | Claim | Evidence | Label | # Claims | Avg. # tokens
---|---|---|---|---|---|---
Train | Test | Claim | Evid.
I | FEVER-sent | Wikipedia | artificial | sent-level | S (52%), R (22%), N (26%) | 145,327 | 19,972 | 9.4 | 35.9
FEVER-para | Wikipedia | artificial | doc-level | S (52%), R (22%), N (26%) | 145,327 | 19,972 | 9.4 | 368.7
VitaminC | Wikipedia | artificial | sent-level | S (50%), R (35%), N (15%) | 370,653 | 63,054 | 12.6 | 29.5
FoolMeTwice | Wikipedia | artificial | sent-level | S (49%), R (51%) | 10,419 | 1,169 | 15.3 | 37.0
II | Climate-FEVER-sent | Climate | natural | sent-level | S (25%), R (11%), N (64%) | 6,140 | 1,535 | 22.8 | 33.8
Climate-FEVER-para | Climate | natural | doc-level | S (47%), R (19%), N (34%) | 1,103 | 278 | 22.9 | 168.9
Sci-Fact-sent | Science | natural | sent-level | S (43%), R (22%), N (35%) | 868 | 321 | 13.8 | 61.9
Sci-Fact-para | Science | natural | doc-level | S (43%), R (22%), N (35%) | 868 | 321 | 13.8 | 257.3
PubHealth | Health | natural | sent-level | S (60%), R (36%), N (4%) | 8,370 | 1,050 | 15.7 | 137.6
COVID-Fact | Forum | natural | sent-level | S (32%), R (68%) | 3,268 | 818 | 12.4 | 82.5
FAVIQ | Question | natural | doc-level | S (50%), R (50%) | 17,008 | 4,260 | 15.2 | 304.9

Table 1: List of the 11 fact verification datasets for our study and their
characteristics.

#### Selection Criteria.

We employ the following criteria to select the datasets for our study.

$\bullet$ We consider both natural and artificial claims in various domains.

$\bullet$ We consider the datasets with human-annotated document-level and
sentence-level evidence. We exclude datasets without evidence or which provide
only non-textual evidence; i.e., tables, knowledge bases, etc.

$\bullet$ We only consider datasets with the binary or the three-class label
annotation due to the difficulty of canonicalizing such multi-class labels.

By conducting a holistic study of fact-checking datasets to date, eight
different data sources meet our requirements. The full list of candidate
datasets we investigate is given in Appendix A.

###  2.2 Dataset Processing

We then process the selected datasets as follows. 1) We convert each dataset
to the unified format of claim-evidence-label triples
${(c_{i},e_{i},l_{i})}_{i=1}^{N}$. The simplicity of this format allows us to
focus on out-of-domain generalization, instead of other orthogonal challenges
of fact-checking. 2) We create separate dataset variants by pairing each claim
with the evidence in different granularity. This enables us to study the
impact of evidence length on generalization. After processing, we obtain the
final selection of 11 datasets used. We now briefly introduce the nature of
each dataset and its specific processing.

#### Group I: datasets with artificial claims.

These are based on Wikipedia articles and are often large in size. However,
crowd-sourced claims are often written with minimal edits to reference
sentences, leading to lexical biases such as the overuse of explicit negation
Schuster et al. (2019).

$\bullet$ FEVER Thorne et al. (2018) asks crowd-workers to mutate sentences
from Wikipedia articles to create claims. We use the Wikipedia paragraph
associated with each claim as its document-level evidence to construct the
FEVER-para dataset. We then use the sentence-level gold evidence for the
supports and refutes claims to build the FEVER-sent dataset. However, since
sentence-level evidence is not available for NEI claims, we use the system of
Malon (2018) to retrieve the evidence sentences, following Atanasova et al.
(2020) and Pan et al. (2021).

$\bullet$ VitaminC Schuster et al. (2021) creates contrastive evidence pairs
for each claim, in which evidence pairs are nearly identical in language and
content, with the exception that one supports a claim while the other does
not.

$\bullet$ FoolMeTwice Eisenschlos et al. (2021) designs a multi-player game
that leads to diverse strategies for crafting claims (e.g., temporal
inference) based on Wikipedia, resulting in more complex claims with less
lexical overlap with the evidence.

#### Group II: datasets with natural claims.

These claims are collected from the Internet and then manually verified by
professional fact checkers. They represent real-world claims, and originate
from diverse domains, such as scholarly documents, news articles, forums, etc.
However, due to the difficulty and high cost of manually verifying real-world
claims, these datasets are limited in scale.

$\bullet$ Climate-FEVER Diggelmann et al. (2020) consists of 1,535 real-life
claims regarding climate-change collected from the Internet. The top five most
relevant sentences from Wikipedia are retrieved as the evidence. Humans then
annotate each sentence as supporting, refuting, or not enough information to
validate the claim. We use the sentence-level annotation as the evidence for
each claim to build the Climate-FEVER-sent. We construct the document-level
evidence for each claim by putting together all of its evidence sentences,
which gives us the Climate-FEVER-para version.

$\bullet$ Sci-Fact Wadden et al. (2020) consists of 1.4K expert-written
scientific claims paired with evidence-containing abstracts annotated with
labels and sentence-level rationale. We use the annotated rationale as the
sentence-level evidence to build the Sci-Fact-sent. We construct the Sci-Fact-
para version by using the evidence-containing abstract as the document-level
evidence for each claim.

$\bullet$ PubHealth Kotonya and Toni (2020) contains 11.8K claims accompanied
by journalist crafted, gold standard judgments to support/refute the claims.
The claims are collected from five fact-checking websites, news headlines, and
news reviews. We use the judgment texts as evidence to pair with each claim.

$\bullet$ COVID-Fact Saakyan et al. (2021) consists of 4,086 claims concerning
the COVID19 pandemic crawled from the r/COVID19 subreddit. We use their
sentence-level evidence annotated by crowd-workers as the evidence.

$\bullet$ FAVIQ Park et al. (2022) contains 26k claims converted from natural
ambiguous questions posed by real users. The answer-containing Wikipedia
paragraph is provided as the document-level evidence for each claim.

Many of the original datasets do not release their test set. Therefore, we use
their original split of train/dev sets as our training and evaluation sets. We
also standardize the naming of labels as supports, refutes, and NEI. We
visualize the global structure of the datasets with tSNE van der Maaten and
Hinton (2008) and analyze the domain divergence in Appendix B.

##  3 Zero/Few-shot Generalization

We now explore the generalization ability of fact verification models across
the 11 datasets. We first formulate the task of zero/few-shot generalization.

#### Task Formulation.

Given a claim $\mathcal{C}$ and a piece of evidence $\mathcal{P}$ as inputs, a
fact verification model $\mathcal{F}$ predicts a label $\mathcal{Y}$ to verify
whether $\mathcal{C}$ is supported, refuted, or can not be verified by the
information in $\mathcal{P}$. In the zero-shot generalization setting, we
train models on one source FV dataset, and then evaluate its performance on a
target test set, without any additional training data in the target dataset.
In the few-shot generalization setting, we assume we have a small amount of
target training examples.

#### Fact Verification Model.

We use the RoBERTa-large Liu et al. (2019) as the benchmark model for our
study since it has achieved state-of-the-art results in many FV datasets. We
concatenate the claim and evidence ([CLS] claim [SEP] evidence) and use it as
input for a classification task to predict the label of the claim. We use the
roberta-large (355M parameters) model provided by the HuggingFace
library222https://huggingface.co/roberta-large.

###  3.1 Zero-shot generalization results

Table 2 shows the zero-shot generalization results in macro-averaged F1 for
the 3-class fact verification task on all the datasets that have
supports/refutes/NEI labels, where we partition by dataset group: Group I, top
(datasets with artificial claims); Group II, bottom (datasets with natural
claims). In general, the RoBERTa model generalizes poorly in this zero-shot
setup. Compared with the in-domain performance (training and testing on the
same dataset), the best zero-shot generalization performance shows a large
drop of 20.80% on average. This shows that the FV model overfits to the
particular dataset and generalizes poorly to unseen datasets. This validates
prior work that shows the neural models are brittle when encountering out-of-
distribution data. Taking a closer look, we further explore several research
questions specific to fact verification behind this general trend.

Train$\downarrow$ Test$\rightarrow$ |  | FEVER
---
-para
| FEVER
---
-sent
VitaminC |  | C-FEVER
---
-para
| C-FEVER
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
PubHealth
FEVER-para | — | 72.81 | 43.87 | 20.83 | 40.90 | 22.09 | 28.10 | 9.05
FEVER-sent | 55.57 | — | 62.11 | 44.98 | 48.70 | 44.98 | 56.15 | 21.61
VitaminC | 52.04 | 65.32 | — | 42.32 | 44.40 | 44.14 | 50.55 | 21.97
C-FEVER-para | 17.86 | 20.04 | 10.59 | — | 42.02 | 29.93 | 31.62 | 5.29
C-FEVER-sent | 17.87 | 24.47 | 20.25 | 54.59 | — | 25.84 | 39.39 | 8.47
SciFact-para | 23.96 | 27.09 | 28.37 | 29.85 | 28.63 | — | 44.68 | 6.78
SciFact-sent | 16.86 | 24.50 | 29.22 | 20.61 | 32.50 | 29.00 | — | 4.49
PubHealth | 35.21 | 34.41 | 30.67 | 34.12 | 24.18 | 40.34 | 42.03 | —
SELF | 85.58 | 89.28 | 86.76 | 44.61 | 62.54 | 52.25 | 54.27 | 72.10

Table 2: Macro-F1 of 3-class fact verification on the evaluation set for all
datasets in a zero-shot generalization setup. Rows correspond to the training
dataset and columns to the evaluated dataset. The row SELF corresponds to the
in-domain performance (training and testing on the same target dataset).

Train$\downarrow$ Test$\rightarrow$ |  | FEVER
---
-para
| FEVER
---
-sent
VitaminC |  | FoolMe
---
Twice
| C-FEVER
---
-para
| C-FEVER
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
PubHealth |  | COVID
---
-Fact
FAVIQ
FEVER-para | — | 94.91 | 71.56 | 72.50 | 77.40 | 76.04 | 72.29 | 75.92 | 44.75 | 56.82 | 55.09
FEVER-sent | 89.08 | — | 79.79 | 84.02 | 74.71 | 80.21 | 75.12 | 87.37 | 58.25 | 63.99 | 61.64
VitaminC | 84.62 | 94.46 | — | 84.57 | 62.80 | 54.59 | 62.37 | 69.31 | 55.32 | 70.32 | 62.98
FoolMeTwice | 82.58 | 91.46 | 78.56 | — | 71.38 | 78.24 | 69.22 | 84.19 | 56.81 | 58.68 | 59.23
C-FEVER-para | 33.37 | 33.72 | 52.56 | 34.15 | — | 56.66 | 39.77 | 40.89 | 38.61 | 25.50 | 33.52
C-FEVER-sent | 51.33 | 62.00 | 55.91 | 50.69 | 75.85 | — | 66.72 | 72.08 | 55.54 | 43.04 | 36.53
SciFact-para | 33.38 | 33.57 | 46.73 | 34.42 | 43.35 | 49.23 | — | 41.27 | 38.69 | 26.64 | 33.69
SciFact-sent | 33.40 | 33.64 | 36.91 | 33.77 | 42.63 | 42.46 | 44.02 | — | 43.35 | 26.64 | 33.51
PubHealth | 65.68 | 64.69 | 53.57 | 53.55 | 53.92 | 61.78 | 68.75 | 71.01 | — | 40.95 | 50.89
COVID-Fact | 70.94 | 76.16 | 37.22 | 63.02 | 44.13 | 51.71 | 63.60 | 76.29 | 60.06 | — | 46.93
FAVIQ | 74.57 | 73.80 | 59.14 | 59.67 | 64.92 | 60.49 | 59.08 | 52.64 | 40.15 | 50.25 | —

Table 3: F1 of binary fact verification on the evaluation set for all datasets
in a zero-shot generalization setup. Rows correspond to the training dataset
and columns to the evaluated dataset.

#### Do artificial claims and natural claims generalize to each other?

The bottom left of Table 2 shows that the model trained on natural claims
generalizes badly to datasets with artificial claims, with an average F1 drop
of 72% relative to the in-domain performance on the three artificial datasets.
In contrast, with natural claims333For fair comparison, we don’t count the
dataset pairs with the same data source, e.g., (SciFact-sent, SciFact-para),
the model generalizes better, with an average F1 drop of 56% (bottom right).
This observation supports the argument that artificial claims and natural
claims have substantial differences, e.g., Wikipedia vs. real-world domains,
high vs. less lexical overlap, and simple vs. diverse reasoning types, as
discussed in § 2.2 and related works Wadden et al. (2020); Saakyan et al.
(2021).

However, a surprising and counter-intuitive observation is that the model
trained on artificial claims generalizes quite well to natural claims. As
shown by the top right section, the average F1 drop narrows to 36.9% when
generalizing from artificial to natural claims, markedly better than when
generalizing between natural claim datasets (56% average drop). In particular,
when trained on FEVER-sent, the model achieves the best generalization results
on 3 out of 5 datasets with natural claims. However, we will show in the
following that the large size of artificial claims contributes a lot to its
good generalization performance.

#### Does generalization improve with more data?

To examine whether good generalization on the FEVER and VitaminC datasets
comes from their large dataset size, we conducted an experiment controlling
for data size. Here, we only take 800 examples for each dataset to train the
model. We show the zero-shot generalization results between the five datasets
with sentence-level evidence in Table 4. The results on all datasets are shown
in Table 10 in Appendix C.

We find that the model trained on natural claim datasets (Group I) can
generalize to other natural claims slightly better than the model trained on
artificial claim datasets (Group II) in this controlled setting. This confirms
that dataset size contributes a lot to generalization ability. Tables 2 and 4
together show that Wikipedia-based artificial claims still generalize well to
natural claims in real-world domains with the growth of dataset size, although
crowd-sourced claims have been criticized to have strong annotation bias and
cannot represent real-life misinformation Schuster et al. (2019).

Train$\downarrow$ Test$\rightarrow$ |  | FEVER
---
-sent
| Vita
---
minC
| C-FEVER
---
-sent
| SciFact
---
-sent
| Pub
---
Health
FEVER-sent | — | 22.20 | 13.66 | 19.64 | 24.57
VitaminC | 16.93 | — | 13.78 | 20.04 | 24.98
C-FEVER-sent | 16.63 | 8.36 | — | 17.24 | 2.51
SciFact-sent | 27.43 | 26.80 | 30.50 | — | 13.06
PubHealth | 28.60 | 26.69 | 18.04 | 22.22 | —

Table 4: Macro-F1 of 3-class fact verification for all datasets with sentence-
level evidence in a zero-shot generalization setup. The size of training data
is controlled to 800 samples for all datasets.

#### Which type of label is more difficult to verify?

Table 5 shows the breakdown of the class-wise F1 score. For each dataset, we
show the average class-wise F1 when training the model on other datasets
(zero-shot) and the class-wise F1 for training on the same dataset (in-
domain). The results show that the refutes claim has the worst prediction
score (in bold) almost for all datasets, in both the zero-shot and the in-
domain setting. The in-domain results are in line with the empirical
observation that Jiang et al. (2020) it is often ambiguous to differentiate
between refutes and NEI claims even for trained human annotators. This
difficulty still maintains in the zero-shot setting and harms the
generalization results.

Dataset | Zero-shot | In-domain
---|---|---
S | R | N | S | R | N
FEVER-para | 33.75 | 25.85 | 34.41 | 87.05 | 85.89 | 83.81
FEVER-sent | 42.52 | 28.24 | 44.38 | 91.32 | 89.42 | 87.10
VitaminC | 50.06 | 20.44 | 25.96 | 94.44 | 89.42 | 76.42
C-FEVER-para | 34.14 | 24.09 | 47.76 | 68.50 | 13.56 | 51.76
C-FEVER-sent | 28.29 | 19.72 | 64.00 | 62.27 | 46.40 | 78.96
SciFact-para | 34.33 | 15.35 | 51.60 | 59.29 | 23.02 | 74.44
SciFact-sent | 47.43 | 22.23 | 55.70 | 61.87 | 18.49 | 82.45
PubHealth | 20.96 | 4.54 | 7.79 | 91.07 | 82.00 | 43.24

Table 5: Class-wise F1 of 3-class fact verification for the zero-shot
generalization setup (left) and the in-domain training setup (right). S:
supports; R: refutes; N: NEI.

#### What is the impact of evidence length?

From Table 2, we find that fact verification in a dataset with document-level
evidence is more difficult than in the same dataset with sentence-level
evidence (an average of 13.29% drop of in-domain F1). This is understandable
since document-level evidence requires the model to additionally filter out
irrelevant information. Climate-FEVER suffers the largest F1 drop of 31.86%,
compared with the slight performance drop on FEVER (4.3%) and SciFact (3.72%).
A possible reason is that Climate-FEVER’s document-level evidence consists of
different (even contradictory) evidence sentences, which requires the model to
reason over multiple sentences instead of just selecting the most relevant
one.

In terms of generalization, the datasets with sentence-level evidence in
general achieve better generalization results than other datasets compared to
their doc-level versions. For example, C-FEVER-sent generalizes better than
C-FEVER-para on 5 of the 6 datasets excluding themselves. Models trained on
sentence-level datasets generalize well to other document-level datasets, but
the converse is not true. These results indicate that training the FV model on
more fine-grained evidence yields better generalization. This is consistent
with the intuition that providing fine-grained evidence eases models’ learning
in FV, showing the importance of accurate evidence retrieval.

###  3.2 Zero-shot generalization for binary FV

Many works Jiang et al. (2020); Saakyan et al. (2021) do not consider NEI
claims due to their ambiguity. To explore whether our previous observations
also hold for the task of binary fact verification, we evaluate the
generalization results for all 11 datasets using only the supports and refutes
claims for training and evaluation, shown in Table 3. In this setting,
artificial claims also generalize well to natural claims in other domains. In
6 of the 7 datasets with natural claims, the best generalization score is from
a model trained on artificial claims. This also holds for the evidence length:
datasets with sentence-level evidence tend to generalize better than document-
level datasets. Finally, compared with the three-class result in Table 2,
generalization improves a lot on Climate-FEVER, SciFact, and PubHealth. The
reason is that the model struggles in distinguishing between refutes and NEI
claims in these datasets, as reflected by Table 5. Therefore, they benefit a
lot from removing the NEI label.

Train$\downarrow$ Test$\rightarrow$ |  | C-fever
---
-para
| C-fever
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
| Pub
---
Health
FEVER-para | 50.04 | 45.99 | 59.91 | 68.18 | 42.81
FEVER-sent | 55.13 | 51.84 | 66.12 | 76.39 | 40.90
VitaminC | 50.41 | 49.80 | 58.27 | 68.59 | 37.84
SELF-few-shot | 22.74 | 10.75 | 17.24 | 33.38 | 43.62
SELF-full | 44.61 | 62.54 | 52.25 | 54.27 | 72.10

Table 6: Macro-F1 of three-class fact verification for all datasets in a few-
shot generalization setup.

Model | Train$\downarrow$ Test$\rightarrow$ |  | FEVER
---
-para
| FEVER
---
-sent
VitaminC |  | C-FEVER
---
-para
| C-FEVER
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
PubHealth
BERT | FEVER-para | — | 64.04 | 33.82 | 18.15 | 29.71 | 18.53 | 18.19 | 3.20
FEVER-sent | 66.97 | — | 54.75 | 35.39 | 26.49 | 39.27 | 39.72 | 25.95
VitaminC | 54.12 | 63.28 | — | 39.57 | 34.93 | 40.80 | 45.51 | 22.21
BioBERT | FEVER-para | — | 67.89 | 42.41 | 24.22 | 38.94 | 37.69 | 35.85 | 8.24
FEVER-sent | 57.18 | — | 51.95 | 40.58 | 39.01 | 36.83 | 38.36 | 37.61
VitaminC | 51.03 | 60.34 | — | 40.60 | 39.72 | 43.38 | 50.71 | 19.44
SciBERT | FEVER-para | — | 68.49 | 39.73 | 20.43 | 33.84 | 28.90 | 35.53 | 6.37
FEVER-sent | 52.95 | — | 51.84 | 35.50 | 35.68 | 34.24 | 39.46 | 36.46
VitaminC | 50.20 | 58.74 | — | 37.99 | 38.79 | 43.55 | 45.69 | 20.66

Table 7: Zero-shot generalization performance (macro-F1) when initialized with
different pretraining models.

###  3.3 Few-shot generalization results

We now consider the few-shot generalization setting, assuming access to a
small number of examples from a target dataset (50 for each class in our
experiment). We pre-train a model on a source dataset and then fine-tune it on
the target dataset. Our goal is to analyze whether pre-training improves
performance compared to training on the target alone.

Table 6 shows the macro-F1 on the evaluation set of all datasets. The rows
“SELF-few-shot” and “SELF-full” show the performance of direct training on the
150 samples of the target dataset and the full target training set,
respectively (without pre-training on the source dataset). Generally, pre-
training on a source FV dataset and fine-tuning to the target outperform
“SELF-few-shot” on all 5 datasets and “SELF-full” on 3 out of 5 datasets. This
shows that pre-training on a related FV dataset helps to reduce the demand for
human-annotated training data in the target domain.

Second, FEVER-sent obtains good generalization performance in all evaluation
datasets. This strengthens our finding in Section 3.1 that FEVER generalizes
well to datasets with natural claims in real-world domains. Last, after
finetuning, we see a dramatic improvement in performance compared to Table 2.
This highlights that current models over-fit the data they are trained on, and
small amounts of data from the target distribution can overcome this
generalization gap.

##  4 Improving Generalization

We then investigate two ways to improve the generalization ability of fact
verification: 1) incorporating domain knowledge via pretraining on specialized
domains, and 2) automatically generating training data via data augmentation.

###  4.1 Pretraining on Specialized Domains

In-domain knowledge is essential for fact-checking in specialized domains. For
example, virology background knowledge is required to verify scientific claims
regarding COVID19 Wadden et al. (2020). When generalizing an FV model from one
domain to another, how to endow the model with such in-domain knowledge is a
challenging subject worthy of long-term study. Here we explore one simple
solution: initializing the model with language models pretrained on
specialized domains.

In Table 7, we show the zero-shot generalization performance when initializing
the FV model with BioBERT Lee et al. (2020) (pretrained on biology literature)
and SciBERT Beltagy et al. (2019) (pretrained on scholarly documents). Our
goal is to explore whether pretraining on specialized domains helps the
generalization. To eliminate the impact of other factors such as the model
size, we use the BERT model Devlin et al. (2019) as the baseline, since
BioBERT and SciBERT are both based on the BERT model.

We find that BioBERT and SciBERT both outperform the BERT on the
generalization scores in Climate-FEVER, SciFact, and PubHealth, with an
average improvement of 21.39% and 12.69% in F1, respectively. However, their
performance on Wikipedia-based datasets (FEVER and VitaminC) is relatively
worse with BERT (-2.6% and -17.7% for BioBERT and SciBERT, respectively). This
confirms the generalization of FV in certain domains (e.g., science) can be
improved with the language models pretrained on relevant domains (e.g.,
scientific papers). We have similar observations for the few-shot
generalization setting, shown in Table 11 in Appendix D. Despite the positive
results, a suitable pretraining model in certain domains (e.g., tweets) is
often unavailable. Moreover, this requires re-training the FV model during
domain transfer. Therefore, how to develop a more accessible and less
expensive way to incorporate in-domain knowledge required for fact-checking
still requires further investigation.

###  4.2 Data Augmentation

Another direction we explore is improving generalization via data
augmentation, which has recently shown promising results in other NLP tasks
such as question answering Yue et al. (2021) and machine translation Cheng et
al. (2020). We first train a claim generation model based on the BART Lewis et
al. (2020), using the (evidence, claim, label) triples in the source domain as
training data. We use the format [LABEL] label [NER] NERs [EVIDENCE] evidence
as the input, and use the claim as the target output for training, where NERs
are the entities appearing in the claim (we add NERs to guide the model to
generate more specific claims). We then apply the trained model to generate
claims with different labels in the target domain by separately assigning
supports, refutes, NEI as the label prefix of the evidence, and we randomly
assign an entity from the evidence as the NERs444Since no ground-truth claim
is available for the target domain, the entity cannot come from the claim. to
guide the claim generation. We name this method as BART-gen.

We train BART-gen on the FEVER-sent dataset and generate synthetic training
(evidence, claim, label) triples for other datasets. For each piece of
evidence in the target domain, we generate six claims with different (label,
NERs) combinations. Table 8 shows the zero- and few-shot generalization
results for BART-gen and other baselines: 1) FEVER-full: the model is trained
on the original FEVER-sent dataset; 2) FEVER-control: the model is trained on
a random subset of FEVER-sent which has the same amount of data with the
generated data; 3) BART-gen: the model is trained on the generated data. For
the few-shot setting, the model is further fine-tuned with 150 in-domain
samples.

Method$\downarrow$ Test$\rightarrow$ |  | C-fever
---
-para
| C-fever
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
| Pub
---
Health
Zero-shot setting |
FEVER-full | 44.98 | 48.70 | 44.98 | 56.15 | 21.61
FEVER-control | 37.14 | 45.64 | 32.42 | 47.57 | 20.69
BART-gen | 46.51 | 51.67 | 47.80 | 54.63 | 69.82
Few-shot setting |
FEVER-full | 55.13 | 51.84 | 66.12 | 76.39 | 40.90
FEVER-control | 37.17 | 48.13 | 62.72 | 77.41 | 47.03
BART-gen | 46.94 | 52.80 | 50.10 | 59.00 | 70.45

Table 8: Macro-F1 of three-class fact verification with data augmentation
(BART-gen) and other baselines. The (top, bottom) shows results for (zero-,
few-)shot generalization, respectively.

For the zero-shot setting results in Table 8, BART-gen consistently improves
the generalization performance compared with FEVER-full (+24.9% in average)
and FEVER-control (+47.4% in average). The results show that training with
generated target data is in general more effective than directly generalizing
a model trained on the source data. This is better reflected by comparing
BART-gen with FEVER-control in which the data amount is the same. The
improvement is especially noticeable for PubHealth, probably because it lacks
the NEI claims in its original training set. Data augmentation addresses this
by generating a sufficiently balanced number of claims for each label.

However, our human evaluation in Appendix E shows that around 30% of generated
claims suffer the label inconsistency problem, i.e., the BART-gen often
generates a fluent claim that does not match our desired label (for example,
we want to generate a refutes claim, but the generated claim is actually NEI).
Label inconsistency may introduce conflicting information between the
pretraining and fine-tuning stages, which we hypothesize is the cause for the
lower level of improvement in fine-tuning the model on the generated data,
compared with fine-tuning the model on FEVER. Therefore, although data
augmentation is a promising direction to improve generalization, it remains a
challenging problem regarding how to generate high-quality claims with
consistent labels.

##  5 Related Work

To overcome the proliferation of misinformation, a great amount of progress
has been made in the area of automated fact verification. For modeling,
pretraining-based models Nie et al. (2019); Stammbach and Neumann (2019); Zhao
et al. (2020); Soleimani et al. (2020) have been used for better text
representation and have achieved promising performance. Graph-based models
Zhou et al. (2019); Liu et al. (2020); Zhong et al. (2020) are used to
facilitate the reasoning over multiple pieces of evidence. However, most
existing models rely on large-scale in-domain training data, which is often
unrealistic for every domain that demand fact checking. In this paper, we aim
to address this by working towards a generalizable fact verification system
that can adapt to different domains with zero or few samples in the target
domain.

For datasets, various fact-checking datasets representing different real-world
domains are proposed, including both naturally occurring Augenstein et al.
(2019); Gupta and Srikumar (2021); Saakyan et al. (2021); Lu et al. (2023) and
human-crafted Thorne et al. (2018); Sathe et al. (2020); Schuster et al.
(2021); Atanasova et al. (2022) fact-checking claims. While these FV datasets
focus on different domains, there is still a substantial overlap in the
abilities required to verify claims across these datasets. However, little
analysis has been done on whether they generalize to one another, and the
extent to which existing datasets can be leveraged for improving performance
on new ones. Similar studies have been done in other NLP tasks Talmor and
Berant (2019); Hardalov et al. (2021), while it is less investigated in fact
verification. In this paper, we bridge this gap by conducting a comprehensive
study of generalization and transfer across existing FV datasets, revealing
several key factors for better generalization.

##  6 Conclusion and Future Work

In this work, we perform a thorough empirical investigation of zero- and few-
shot generalization over 11 fact verification datasets. Moreover, we conduct
an exhaustive analysis and highlight the most important factors influencing
the generalization performance. We further empirically explore two ways to
improve generalization in fact verification. We highlight several practical
takeaways:

$\bullet$ Overall, the FV model generalizes poorly to unseen datasets compared
with in-domain evaluation. However, performance is largely improved by fine-
tuning on the target data.

$\bullet$ Artificial claims can also generalize well to natural claims with an
increase of dataset size.

$\bullet$ Model trained on sentence-level evidence generalize better than
document-level evidence.

$\bullet$ The refutes claims are the most difficult to verify among the three
labels.

$\bullet$ Domain-specific pretraining and data augmentation consistently
improves generalization performance, but they also leave unsolved challenges.

In future work, we plan to experiment with more datasets, including non-
English ones. We will also explore the generalization of other sub-tasks in
fact-checking, e.g., claim detection, evidence retrieval.

## Limitations

In our study, we primarily focused on assessing the generalization
capabilities of Transformer-based models, such as RoBERTa. However, we did not
extend our evaluation to include zero- and few-shot learning performance on
large language models (LLMs) like InstructGPT Ouyang et al. (2022) and GPT-4
OpenAI (2023), due to the high experimental costs. Recently, these LLMs have
demonstrated impressive few-shot learning capacities across a variety of
natural language processing tasks, including few-shot fact-checking Pan et al.
(2023). However, they are API-based and function as black-box models. This
restricts our ability to delve deeper into their behavior, given that we
cannot access their model weights or fine-tune them directly. On the contrary,
Transformer-based models are open-sourced and replicable, providing a wealth
of opportunity for more profound insights into our study and paving the way
for future research.

## Acknowledgements

This research / project is supported by the Ministry of Education, Singapore,
under its MOE AcRF TIER 3 Grant (MOE-MOET32022-0001). Any opinions, findings
and conclusions or recommendations expressed in this material are those of the
author(s) and do not reflect the views of the Ministry of Education,
Singapore.

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Dataset | Domain | Claim |  | Doc-level
---
evidence
| Sent-level
---
evidence
| NEI
---
claims
| Publicly
---
available
FEVER Thorne et al. (2018) | Wikipedia | artificial | ✓ | ✓ | ✓ | ✓
WikiFactCheck Sathe et al. (2020) | Wikipedia | artificial | ✓ | ✓ |  | ✓
HOVER Jiang et al. (2020) | Wikipedia | artificial | ✓ | ✓ |  | ✓
VitaminC Schuster et al. (2021) | Wikipedia | artificial | ✓ | ✓ | ✓ | ✓
Fool Me Twice Eisenschlos et al. (2021) | Wikipedia | artificial | ✓ | ✓ |  | ✓
CREAK Onoe et al. (2021) | Commonsense | artificial | ✓ |  |  | ✓
CreditAccess Popat et al. (2016) | News | natural | ✓ | ✓ |  | ✓
Emergent Ferreira and Vlachos (2016) | Emergent | natural | ✓ | ✓ | ✓ | ✓
MultiFC Augenstein et al. (2019) | Multiple | natural |  |  | ✓ | ✓
Snopes Hanselowski et al. (2019) | News | natural | ✓ | ✓ | ✓ |
Climate-FEVER Diggelmann et al. (2020) | Climate | natural | ✓ | ✓ | ✓ | ✓
SciFact Wadden et al. (2020) | Scientific | natural | ✓ | ✓ | ✓ | ✓
PubHealth Kotonya and Toni (2020) | Health | natural | ✓ | ✓ | ✓ | ✓
COVID-Fact Saakyan et al. (2021) | Forum | natural | ✓ | ✓ |  | ✓
X-Fact Gupta and Srikumar (2021) | Multiple | natural | ✓ |  | ✓ | ✓
FaVIQ Park et al. (2022) | Forum | natural | ✓ | ✓ |  | ✓

Table 9: A list of candidate fact verification datasets. Figure 1: tSNE plot
of [CLS] representations of each dataset; highlighted points denote cluster
centroids.

##  Appendix A A List of Fact Verification Datasets

In Table 9 we provide a comprehensive list of candidate datasets that we
consider for our study, including those are not selected in our benchmark in
the end. The candidate list does not include the fact checking datasets
without providing evidence for the claim (e.g., FakeNewsNet Shu et al.
(2020)), or focusing on non-textual evidence such as table (e.g., FEVEROUS Aly
et al. (2021) and TabFact Chen et al. (2020)).

Afterward, we exclude some datasets from the candidate list, mainly because of
the lack of clean evidence, the small scale in size, non-English claims, and
unavailability. For example, we exclude Emergent Ferreira and Vlachos (2016)
since it only contains 300 claims. We exclude X-Fact Gupta and Srikumar (2021)
since it is a multi-lingual dataset that mainly focus on non-English
languages. Snopes Hanselowski et al. (2019) is not included since it is not
publicly available. We also exclude CREAK Onoe et al. (2021), HOVER Jiang et
al. (2020), and MultiFC Augenstein et al. (2019) since their evidence is
either coarse-grained (e.g., the whole Wikipedia page) or noisy (e.g., the
original webpage in certain fact checking website).

##  Appendix B Domain Divergence Analysis

Following Hardalov et al. (2021), we plot the 11 datasets in a latent vector
space to visualize the global structure of the datasets. We proportionally
sample 82K (10%) examples, and we pass them through a RoBERTa-large Liu et al.
(2019) model without any training. The input has the following form: [CLS]
claim [SEP] evidence. Next, we take the [CLS] token representations, and we
plot them in Figure 1 using tSNE van der Maaten and Hinton (2008). We can see
that datasets with natural claims are grouped top-right, clearly separated
from those with artificial claims. The clusters of real-world domain datasets
do not overlap, which highlights the rich diversity of our selected datasets.
We also notice that datasets with sentence-level evidence have little overlap
with their paragraph-level counterparts (e.g., Climate-FEVER-sentence v.s.
Climate-FEVER-paragraph). To sum up, Figure 1 confirms that there exists
divergence between different domains and datasets.

Train$\downarrow$ Test$\rightarrow$ |  | FEVER
---
-para
| FEVER
---
-sent
VitaminC |  | C-FEVER
---
-para
| C-FEVER
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
PubHealth
FEVER-para | — | 31.98 | 27.58 | 32.83 | 25.22 | 25.36 | 30.72 | 18.82
FEVER-sent | 16.68 | — | 22.20 | 21.78 | 13.66 | 20.01 | 19.64 | 24.57
VitaminC | 16.70 | 16.93 | — | 22.01 | 13.78 | 20.04 | 20.04 | 24.98
C-FEVER-para | 17.15 | 18.00 | 27.51 | — | 31.44 | 17.24 | 18.19 | 5.04
C-FEVER-sent | 16.63 | 16.63 | 8.36 | 17.51 | — | 17.24 | 17.24 | 2.51
SciFact-para | 30.66 | 30.66 | 28.30 | 33.46 | 31.36 | — | 42.40 | 12.15
SciFact-sent | 28.37 | 27.43 | 26.80 | 27.93 | 30.50 | 24.51 | — | 13.06
PUBHEALTH | 26.77 | 28.60 | 26.69 | 28.25 | 18.04 | 22.80 | 22.22 | —
SELF | 32.35 | 16.68 | 29.53 | 36.12 | 26.14 | 53.33 | 50.72 | 52.05

Table 10: Macro-F1 of 3-class fact verification on the evaluation set for all
datasets in a zero-shot generalization setup. The size of training data is
controlled to 800 samples for all datasets. Rows correspond to the training
dataset and columns to the evaluated dataset. The row SELF corresponds to the
in-domain performance (training and testing on the same target dataset).

##  Appendix C Full Results of Controlled Size Generalization

Table 10 shows the full results of the controlled experiment in Section 3.1
where we only take 800 examples for each dataset to train the model. We find
that the model trained on artificial claim datasets generalize slightly worse
to natural claims compared with the model trained on artificial claim datasets
in the controlled size setting. Compared with the good generalization results
from artificial claims to natural claims in Table 2, it shows that the size of
the source dataset contributes a lot to generalization ability of fact
verification.

Model | Train$\downarrow$ Test$\rightarrow$ |  | C-FEVER
---
-para
| C-FEVER
---
-sent
| SciFact
---
-para
| SciFact
---
-sent
PubHealth
BERT | FEVER-para | 41.24 | 42.68 | 42.74 | 43.42 | 15.57
FEVER-sent | 43.84 | 45.09 | 50.23 | 55.65 | 33.45
VitaminC | 45.94 | 43.38 | 57.25 | 58.12 | 33.69
BioBERT | FEVER-para | 46.22 | 43.06 | 61.45 | 59.19 | 33.47
FEVER-sent | 47.64 | 43.43 | 48.59 | 53.17 | 39.44
VitaminC | 44.16 | 40.48 | 54.52 | 61.62 | 32.29
SciBERT | FEVER-para | 47.92 | 40.46 | 53.12 | 56.83 | 34.39
FEVER-sent | 43.92 | 40.94 | 56.53 | 60.49 | 42.14
VitaminC | 46.23 | 40.16 | 60.95 | 61.04 | 37.37

Table 11: Few-shot generalization performance (macro-F1) when initialized with
different pretraining models.

##  Appendix D Full Results of Few-shot Generalization

In Table 11, we show the few-shot generalization performance of FV model
pretrained on specialized domains. After finetuning, we observe dramatic
improvement in performance comparing to Table 7 (+14.31% for BERT, +11.84% for
BioBERT, +15.29% for SciBERT). Under few-shot setting, we find that BioBERT
and SciBERT still outperform the BERT on the generalization scores in all
datasets except Climate-FEVER-sentence.

##  Appendix E Human Evaluation of Generated Claims

We conduct the human evaluation on the claims generated by BART-gen on four
datasets: Climate-FEVER-sentence, Sci-Fact-sentence, PubHealth, and COVID-
Fact. We randomly sample $90$ generated claims for each dataset with a
balanced desired label distribution. To be specific, 30/30/30 of their desired
labels, i.e., the type of claim we expect the model to generate (by appending
the corresponding label prefix) are supports/refutes/NEI. We ask two expert
human annotators to annotate the actual label for each claim, i.e., whether
the generated claim is supported, refuted, or cannot be verified by the
evidence. If the generated claim is an incomplete or unreadable sentence, we
label it as Unclassified.

Figure 2 shows the confusion matrix for the desired labels and the actual
labels. We find that in all four datasets, around 30% of the generated claims
suffer from the label inconsistency problem, i.e., the actual label of the
claim is not the desired label. Specially, the confusion between the refutes
and NEI claim is the major type of error, showing that refutes and NEI claims
are the hardest for the model to generate.

We also observe that around 5% of the generated claims are incomplete or
unreadable. Moreover, most generated claims are short and simple (e.g.,
“Gilbert Rothschild was a person”), which do not require complex reasoning to
verify. It is therefore worthy to investigate how to obtain high-quality
claims in data augmentation for better generalization in the future study.

(a) Climate-FEVER-sentence

(b) SciFact-sentence

(c) PubHealth

(d) COVID-Fact

Figure 2: Confusion matrices (normalized over columns) of generated claims on
four datasets. The desired label is the input label for our claim generation
model (BART-gen) and the actual label is the human-annotated label.
“Unclassified” means that the generated claim is incomplete or unreadable.