# “Why is this misleading?”: Detecting News Headline
Hallucinations with Explanations

Jiaming Shen  Google Research<EMAIL_ADDRESS>,  Jialu Liu  Google Research
<EMAIL_ADDRESS>,  Dan Finnie  Google<EMAIL_ADDRESS>,  Negar Rahmati
Google<EMAIL_ADDRESS>,  Michael Bendersky  Google Research
<EMAIL_ADDRESS>and  Marc Najork  Google Research<EMAIL_ADDRESS>
(2023)

###### Abstract.

Automatic headline generation enables users to comprehend ongoing news events
promptly and has recently become an important task in web mining and natural
language processing. With the growing need for news headline generation, we
argue that the _hallucination issue_ , namely the generated headlines being
not supported by the original news stories, is a critical challenge for the
deployment of this feature in web-scale systems Meanwhile, due to the
infrequency of hallucination cases and the requirement of careful reading for
raters to reach the correct consensus, it is difficult to acquire a large
dataset for training a model to detect such hallucinations through human
curation. In this work, we present a new framework named ExHalder to address
this challenge for headline hallucination detection. ExHalder adapts the
knowledge from public natural language inference datasets into the news domain
and learns to generate natural language sentences to explain the hallucination
detection results. To evaluate the model performance, we carefully collect a
dataset with more than six thousand labeled $\langle$article,
headline$\rangle$ pairs. Extensive experiments on this dataset and another six
public ones demonstrate that ExHalder can identify hallucinated headlines
accurately and justifies its predictions with human-readable natural language
explanations.

Hallucination Detection, Natural Language Explanation

††copyright: acmcopyright††journalyear: 2023††copyright:
rightsretained††conference: Proceedings of the ACM Web Conference 2023; May
1–5, 2023; Austin, TX, USA††booktitle: Proceedings of the ACM Web Conference
2023 (WWW’23), May 1–5, 2023, Austin, TX, USA††doi:
10.1145/3543507.3583375††isbn: 978-1-4503-9416-1/23/04††ccs: Computing
methodologies Natural language processing††ccs: Information systems Web
applications

##  1\. Introduction

With tens of millions of news articles published every day on the web (Center,
2021), people are inundated with massive news contents and find them hard to
digest. To facilitate more efficient and user-friendly news content
consumption, recent works in the industry propose to generate headlines from
either a single news article (Ao et al., 2021) or a set of news articles
related to the same event (Gu et al., 2020). The generated news headline is
intended to serve as a succinct, informative, and accurate summary of its
underlying news article(s), and thus it helps the users to quickly grasp the
gist of a news story.

Figure 1. An illustrative example of automated news headline hallucination
detection with a model generated natural language explanation.

To obtain high-quality news headlines, early studies (Mohammadzadeh et al.,
2012; Kochetkova et al., 2018; Higurashi et al., 2018) propose _extractive_
methods to first extract words from the article title and then organize those
salient words into the output headline. More recently, with the advances of
natural language generation research (Rush et al., 2015; Tang et al., 2022;
Zhang et al., 2020), more _abstractive_ methods are developed to directly
summarize the news article into a concise news headline (Tan et al., 2017;
Bukhtiyarov and Gusev, 2020; Gu et al., 2020; Ao et al., 2021; Li et al.,
2022). These abstractive summarization methods typically adopt the encoder-
decoder architecture (Cho et al., 2014; Sutskever et al., 2014) where the
encoder synthesizes the knowledge in the news article using vector
representations and the decoder outputs the generated headline in a word-by-
word fashion. Although overall quality improvements have been made by this
approach, people observe that these generation models often will output
hallucinated headlines that are not supported by the underlying news articles.
For example, in Figure 1, the generation model outputs the headline “
_Cambridge university to stop online lectures_ ” based on the article with the
title “Cambridge University moves all lectures online until summer 2021”. The
generated headline is misleading because it suggests that Cambridge University
will stop online lectures instead of moving some face-to-face lectures online
until the summer of 2021.

In this paper, we study the _news headline hallucination detection_ task:
given a pair of $\langle$news article, news headline$\rangle$, we aim to
algorithmically determine if the headline is supported by the underlying
article and thus is not misleading. Figure 1 shows an example where the news
article indicates Cambridge University will move in-person lectures online for
a period of time but the generated news headline suggests the opposite.
Therefore, this is a misleading headline and the hallucination detector should
predict this headline as “not supported”. An intuitive approach to this task
is to train a classifier using a large set of $\langle$article,
headline$\rangle$ pairs with their hallucination labels. However, as those
hallucination cases appear infrequently and require deep reading
comprehension, such a labeled dataset is usually of small scale and thus
forbids us from learning a powerful model that can capture the subtle semantic
differences between news articles and news headlines.

To tackle the lack-of-supervision challenge, we propose a novel framework
named ExHalder, standing for “Explanation-enhanced Headline Hallucination
detector”. ExHalder is developed based on two key ideas. First, we observe
that there exist many similarities between the headline hallucination
detection (HHD) task and the natural language inference (NLI) (MacCartney,
2009; Bowman et al., 2015) task. For example, both of them aim to detect if
one piece of text (“headline” in the HHD task and “hypothesis” in the NLI
task) is supported/entailed by another piece of text (“article” in the HHD
task and “premise” in the NLI task). Based on this observation, we propose to
pretrain ExHalder using public large-scale NLI datasets (Bowman et al., 2015;
Camburu et al., 2018; Yin et al., 2021) and transfer the knowledge learned
from the NLI task to the headline hallucination detection task. Second, as the
framework name suggests, we propose to go beyond the binary class label and
utilize natural language explanations to augment the model learning process.
These explanations are particularly useful in the low resource setting (i.e.,
with limited training data) and help models to generalize better. We
demonstrate that the learned ExHalder can generate high-quality human-readable
explanations to justify its prediction results. Take the case in Figure 1 for
example, ExHalder not only predicts the headline is “not supported” by the
news article but also justifies the output with an explanation “because Stop
online lectures vs move all lectures online until summer 2021”.

To make the best use of these explanations, ExHalder includes three key
components: (1) a _reasoning classifier_ which receives as input the
$\langle$article, headline$\rangle$ pair and outputs the class label along
with the label explanation, (2) a _hinted classifier_ which receives as input
the $\langle$article, headline, explanation$\rangle$ triplet and predicts the
class label, and (3) an _explainer_ that generates the natural language
explanation based on the input $\langle$article, headline$\rangle$ with its
known class label. These three components utilize the explanation signals from
different angles and work collaboratively within our ExHalder framework.
Specifically, during the training phase, we will train the explainer to
generate more explanations and use them to augment the original training set
for learning the reasoning classifier and the hinted classifier. At the
inference stage, we first input the test $\langle$article, headline$\rangle$
tuple into the reasoning classifier to obtain its predicted class and
generated explanation. Then, we concatenate the explanation with the input
tuple and feed them together into the hinted classifier to obtain another
class prediction. Finally, we aggregate these two predictions and return the
final predicted class with its corresponding explanation.

We test the effectiveness of the ExHalder framework on seven hallucination
detection datasets from different domains. Our results demonstrate that
ExHalder achieves state-of-the-art performance in terms of detection accuracy,
recall, and F1 score. Furthermore, we show that ExHalder can generate high-
quality natural language explanations to justify its prediction results.

Figure 2. Key Components of ExHalder framework.

Contributions. To summarize, our major contributions include: (1) a novel
framework that automatically detects news headline hallucinations with limited
manually labeled data; (2) an effective method for integrating natural
language explanations into the detection pipeline and enabling the model to
generate human-readable explanation; (3) a real-world headline hallucination
detection datasets curated by news-domain experts; and (4) extensive
experiments on seven real-world datasets that verify both the hallucination
detection accuracy and the generated explanation quality.

The rest of the paper is organized as follows. Section 2 discusses the related
work. Section 3 formalizes our problem. Then, we present our ExHalder
framework in Section 4 and conduct experiments in Section 5. Finally, we
conclude this paper in Section 6.

##  2\. Related Work

News Headline Generation. Automated news headline generation, widely
considered as a special form of document summarization task, aims to generate
a headline-style summary from either a single news article (Murao et al.,
2019; Ao et al., 2021) or a set of news articles related to the same event (Gu
et al., 2020). Early studies address this task by adopting an extractive
approach (Mohammadzadeh et al., 2012; Kochetkova et al., 2018) that first
selects words from the article and then organizes them into the output
headline via statistical models (Banko et al., 2000; Schwartz et al., 2002;
Dorr et al., 2003). This approach achieves limited success as some extracted
words are incoherent (Alfonseca et al., 2013) and the traditional statistical
models lack expressive powers to generate vivid text. Recently, the advances
of natural language generation research (Rush et al., 2015; Tang et al., 2022;
Zhang et al., 2020) lead to more abstractive headline generation methods
(Gavrilov et al., 2019; Gu et al., 2020; Bukhtiyarov and Gusev, 2020; Ao et
al., 2021; Li et al., 2022). They adopt the encoder-decoder architecture (Cho
et al., 2014; Sutskever et al., 2014) where the encoder synthesizes the
knowledge in the news article(s) using vector representations and the decoder
outputs the generated headline in a word-by-word fashion with potential
constraints (e.g., length control (Kikuchi et al., 2016), keyword preservation
(Mao et al., 2020), or style preference (Xu et al., 2019)). Although the
overall quality improvements have been made, people observe that these
generation models often will output hallucinated headlines that are not
supported by the underlying news articles (Xiao and Wang, 2021; Ji et al.,
2022). This hallucination issue becomes a key blocker for deploying web-scale
automated headline generation models in industry, which motivates us to study
the news headline hallucination detection problem in this work.

Hallucination Detection. Recent years have witnessed the great improvements of
many natural language generation (NLG) models. One remaining challenge for
deploying these NLG models in real-world systems is the hallucination issue
that refers to the scenario where the generated content being nonsensical or
unfaithful to the provided source content (Magdy and Wanas, 2010; Filippova,
2020; Ji et al., 2022). Many studies propose to mitigate the hallucination
issue by either cleaning the model training data (Gardent et al., 2017;
Rebuffel et al., 2021) or learning a classifier to postprocess/filter
generated contents (Cao et al., 2020; Chen et al., 2021; Rashkin et al.,
2021). In a boarder sense, our study falls into the second category and
further enhances the classifier with a natural language explanation component.

Natural Language Inference. The task of natural language inference (Bowman et
al., 2015) (also called textual entailment (Dagan et al., 2005; Bar-Haim et
al., 2006)) aims to predict if a given “premise” text entails, contradicts, or
is neutral with regard to another “hypothesis” text. As this task can measure
the model’s language reasoning capability and has multiple large datasets
(Bowman et al., 2015; Williams et al., 2018; Yin et al., 2021), there have
been studies on how to adapt it for other language tasks such as weakly-
supervised classification (Shen et al., 2021), sentence embedding learning
(Conneau et al., 2017), and fact checking (Sathe and Park, 2021). Among these
studies, the most relevant are those utilizing trained NLI models for
measuring the faithfulness of summarization methods (Thorne et al., 2018;
Maynez et al., 2020; Pagnoni et al., 2021). However, different from this work,
they do not leverage the explanation information. In contrast, our experiments
show that these explanations can help better transfer the knowledge from the
NLI task to the headline hallucination detection task.

Natural Language Explanation. Leveraging natural language explanations to
improve machine learning models has long been studied in the literature.
Typical usages include feeding the human-written explanations as additional
input signals (Liang et al., 2020; Hase et al., 2020) or treating them as
model outputs and training the model to reproduce them (Narang et al., 2020).
Although how models benefit from these explanations still remains an active
research problem (Hase and Bansal, 2022), the general finding is that these
natural language explanations could be particularly useful when only limited
amount of labeled data are provided (Lee et al., 2020; Ye et al., 2020; Wei et
al., 2022). In this work, we study how to effectively leverage these
explanations to enhance the hallucination detection accuracy and explore the
possibility of generating free-text explanations to justify model’s reasoning
rationale.

##  3\. Problem Formulation

In this section, we first introduce the notations used later in the paper and
then present our problem formulation.

Notations. A news _article_ $\mathbf{d}\in\mathcal{D}$ is a document composed
of a token sequence $[d_{1},d_{2},\dots]$. A news _headline_
$\mathbf{h}\in\mathcal{H}$ is a succinct summary of the news article,
represented by another token sequence $[h_{1},h_{2},\dots]$. Although the news
headline is typically generated based on the news article, those generation
methods sometimes encounter the _hallucination_ issue, namely the generated
headline is not entailed by its corresponding news article. Given a pair of
article and headline $\langle\mathbf{d}_{i},\mathbf{h}_{i}\rangle$, we use
$s_{i}\in\mathcal{S}=\\{0,1\\}$ to indicate if the headline is supported by
the article or not. Optionally, we may have a natural language explanation to
elucidate why the article supports or contradicts the headline. We use another
token sequence $\mathbf{e}=[e_{1},e_{2},\dots]$ to denote this free-text
explanation.

Problem Definition. The task of _news headline hallucination detection_ is to
learn a predictor
$\mathbf{f}(\cdot):\mathcal{D}\times\mathcal{H}\rightarrow\mathcal{Y}$ that
takes a pair of $\langle$news article, news headline$\rangle$ as the input and
predicts if the news headline is supported by the news article. Based on the
available resources for learning the predictor $\mathbf{f}(\cdot)$, we further
consider two settings: (1) supervised setting where we have a small set of $N$
labeled examples $\\{\mathbf{d}_{i},\mathbf{h}_{i},s_{i}\\}|_{i=1}^{N}$ to
train the predictor, and (2) zero-shot setting where we do not have any
labeled example and have to exploit knowledge from other related tasks.

##  4\. ExHalder: Explanation-Enhanced Headline Hallucination Detector

In this section, we first introduce three key components of our ExHalder
framework. Then, we elaborate on how ExHalder utilizes these components for
news headline hallucination detection and how to train the ExHalder framework.
Finally, we discuss the inference procedure of ExHalder framework.

Figure 3. The ExHalder framework overview.

###  4.1. Key Components of ExHalder Framework

In this work, we adopt the widely used encoder-decoder architecture (Cho et
al., 2014; Sutskever et al., 2014) due to its strong representation power and
wide applicability for both classification and generation tasks. The encoder
first compresses the information of an input sequence
$\mathbf{x}=[x_{1},x_{2},\dots]$ into its vector representation and then the
decoder generates tokens in the output sequence
$\mathbf{y}=[y_{1},y_{2},\dots]$ one at a time. Specifically, given the full
input sequence $\mathbf{x}$ and the output sequence prefix
$\mathbf{y}_{1:i-1}=[y_{1},y_{2},\dots,y_{i-1}]$, we produce the token $y_{i}$
as follow:

(1)
$\mathbf{P}(y_{i}|\mathbf{x},\mathbf{y}_{1:i-1})=\frac{\exp(\mathbf{v}_{i}\cdot\mathbf{E}(y_{i}))}{\sum_{y^{\prime}\in\mathcal{V}}\exp(\mathbf{v}_{i})\cdot\mathbf{E}(y^{\prime}))},$

where $\mathbf{v}_{i}$ is the decoder output hidden vector corresponding to
token $y_{i}$, $\mathbf{E}(y)$ is the embedding of a token $y$, and
$\mathcal{V}$ denotes the entire vocabulary.

Although initially proposed for generation tasks, the encoder-decoder models
can also be applied to classification problems by (1) choosing one special
token for each possible class; (2) forcing the model to do one-step decoding;
and (3) mapping the output token $y_{1}$ to its corresponding class as the
final prediction (Raffel et al., 2020). Take our hallucination detection task
as an example. We can use the special token ‘$c$’ in vocabulary $\mathcal{V}$
to represent the “contradictary” class and compute the hallucination
probability as $\mathbf{P}(y_{1}=\text{`}c\text{'}|\mathbf{x})$.

In the ExHalder framework, given a pair of $\langle$article, headline$\rangle$
($\langle\mathbf{d}_{i}$, $\mathbf{h}_{i}\rangle$) along with its class label
$s_{i}$ and label explanation $\mathbf{e}_{i}$, we define the following three
components based on how we construct their input sequences $\mathbf{x}$ and
expect the output sequences $\mathbf{y}$ will be. Figure 2 shows an
architecture overview of these three components.

Reasoning Classifier. The input sequence $\mathbf{x}$ is of format “headline
entailment: headline: <HEADLINE> article: <ARTICLE>” where the “<HEADLINE>”
and “<ARTICLE>” are two placeholders and will later be replaced with the
contents in the news headline $\mathbf{h}$ and the news article $\mathbf{d}$.
The output sequence $\mathbf{y}$ is of format “<CLASS> because <EXPLANATION>”
where the placeholder token “<CLASS>” (one of {“Entail, “Contradict”})
indicates if the news article entails or contradicts the news headline, and
the placeholder token “<EXPLANATION>” corresponds to the natural language
explanation $\mathbf{e}$. When the rater does not provide any explanation for
the labeled example during the curation process, this “<EXPLANATION>” token
could simply be an empty string. Note here if we throw away the “because
<EXPLANATION>” part in the output sequence $\mathbf{y}$, the reasoning
classifier will degenerate into a standard classifier with the encoder-decoder
architecture.

Hinted Classifier. As its name suggests, the input of hinted classifier goes
beyond the one used for the reasoning classifier and includes the natural
language explanation as the “hint”. Specifically, we append a string “comment:
<EXPLANATION>” after the reasoning classifier’s input and teach the model to
output a single token “<CLASS>” to indicate the final predicted class. The
hinted classifier is expected to achieve better classification performance
than the reasoning classifier because (1) its input contains more signals from
the additional “comment: <EXPLANATION>” part, and (2) it does not waste
representative power for the explanation generation.

Explainer. Different from the previous two “classifiers”, the explainer inputs
a sequence that already contains the class information and aims to output a
natural language sentence to explain this class. Specifically, the input
sequence of the explainer is of format “headline entailment: headline:
<HEADLINE> article: <ARTICLE> <CLASS> because” and the output sequence will be
just the natural language explanation itself.

###  4.2. The ExHalder Framework

Our ExHalder framework is built upon the above three key components for news
headline hallucination detection. As both the reasoning classifier and the
hinted classifier contain the prediction result “<CLASS>” in their outputs,
one may argue that we can directly adopt supervised learning techniques to
train these two classifiers for hallucination detection. This approach,
however, requires massive labeled data which are often inaccessible for real-
world applications. Therefore, in this work, we propose two novel techniques
to address such a label data scarcity issue: (1) pretraining with large-scale
natural language inference (NLI) datasets, and (2) augmented training with
human-written explanations. Figure 3 shows an overview of our ExHalder
framework.

####  4.2.1. NLI-based Pretraining.

The natural language inference (NLI) task aims to predict if a given
“hypothesis” is supported/entailed by another input “premise” text. Take the
case in Figure 3 as an example, the hypothesis “ _A man pushes a cart_ ” is
supported by the premise “ _A man with a beige jacket carries a water jug and
pushes a food cart._ ” and thus the target class is “Entail”. We observe that
this NLI task shares many similarities with our news headline hallucination
detection (HHD) task. Both of them aim to detect if one piece of text
(“headline” in the HHD task and “hypothesis” in the NLI task) is
supported/entailed/grounded by another piece of text (“article” in the HHD
task and “premise” in the NLI task). Such a connection enables us to transfer
knowledge from the NLI task to our news domain HHD task. Furthermore,
different from the case in the news domain HHD with limited labeled data,
there are many large-scale publicly available NLI datasets (Bowman et al.,
2015; Williams et al., 2018; Camburu et al., 2018; Nie et al., 2020; Yin et
al., 2021).

Based on the above observation, in this work, we propose to pretrain all the
components in ExHalder using the NLI datasets. Specifically, we use the eSNLI
(Camburu et al., 2018) and ANLI (Nie et al., 2020) datasets for pretraining as
they both contain human written natural language explanations. Given a NLI
example $\langle$hypothesis, premise, label$\rangle$, we first construct one
training example by replacing the “<HEADLINE>” and the “<ARTICLE>” placeholder
tokens with the “hypothesis” text and the “premise” text, respectively. Then,
we train our reasoning classifier, hinted classifier, and explainer models
using the standard teacher-forcing technique (Williams and Zipser, 1989).

####  4.2.2. Explainer-augmented Training.

Due to language variability, people have different ways to express the same
underlying rationale. However, in the existing NLI datasets, due to
constrained manual curation resources, each example has only a very limited
amount of human-written explanation(s) (e.g., 1 for the eSNLI dataset and 1-3
for the ANLI dataset). To obtain more explanations and use them to train the
hinted classifier and the reasoning classifier, we propose to augment the
existing NLI datasets with a learned explainer. Specifically, after the
initial pretraining stage, we use the learned explainer to generate $K$
additional explanations for each NLI example. Then, we merge these augmented
examples with the examples in the original NLI dataset and continue to train
the hinted classifier and reasoning classifier with this augmented dataset.
More training details are discussed in the experiment section.

####  4.2.3. Optional Domain Fine-tuning.

For both the NLI-based pretraining step and the explainer-augmented training
step, we only use the general domain datasets. When additional news domain-
specific datasets are available, we can follow the same procedure above and
further fine-tune the components in our ExHalder framework. In this work, we
collect a new headline hallucination dataset and perform this domain fine-
tuning step in one of our experiment settings (c.f. Section 5.1).

###  4.3. ExHalder Inference

At the inference stage, we are given a test $\langle$article,
headline$\rangle$ pair and apply the learned hinted classifier and reasoning
classifier to make a prediction. Specifically, we first feed the test example
into the reasoning classifier and parse its output sequence into the predicted
class and the explanation sentence. Then, we concatenate this generated
explanation with the original headline and article and treat it as the input
sequence of the hinted classifier. We use the hinted classifier to obtain
another class prediction. Finally, we use a combiner to aggregate the
predictions from the reasoning classifier and the hinted classifier. Here,
without requiring more labeled examples, we adopt a simple averaging strategy
for the combiner. Namely, we average the probability scores from the reasoning
classifier and the hinted classifier and return this averaged score as the
final prediction probability111When more labeled examples are available,
another combiner design is to train a small model to calibrate and aggregate
the probability scores from both the reasoning classifier and the hinted
classifier..

##  5\. Experiments

In this section, we study the performance of ExHalder on two settings: (1)
_supervised setting_ where we have a small set of labeled $\langle$article,
headline$\rangle$ pairs for model learning, and (2) _zero-shot setting_ where
no labeled data is provided.

###  5.1. News Headline Hallucination Detection with Supervision

####  5.1.1. Dataset.

To the best of our knowledge, there is no publicly available news headline
hallucination detection dataset. Therefore, in this paper, we collect a new
dataset that contains 6270 human curated examples: 5190 examples for training,
349 examples for validation, and 731 examples for testing. Each example
includes a triplet of $\langle$news article, news headline, hallucination
label$\rangle$ where the headline is generated from NHNet (Gu et al., 2020)
and the label is obtained from multiple human experts according to a common
guideline. Specifically, we ask three full-time journalism degree holders in
the news domain to rate each example and determine the final hallucination
label through majority voting. Among these examples, 1934 of them are labeled
as "hallucinated" and the remaining 4336 examples are labeled as "entailed".
Furthermore, there are 2074 examples with additional rater-written comments
(besides binary hallucination labels) and we treat them as user-provided
explanations. The dataset is publicly available at: https://bit.ly/exhalder-
dataset.

####  5.1.2. Compared Methods.

We compare the following methods for the headline hallucination detection
task:

  * •

SVM (Cortes and Vapnik, 2004): We manually extract a set of features based on
the textual string of the news headline and the news article (e.g., their
corresponding sequence lengths, the number of overlapping words, some word-
level editing distances like Jaro-Winkler distance (Cohen et al., 2003),
_etc._), and train a standard SVM model with the RBF kernel for predictions.

  * •

XGBoost (Chen and Guestrin, 2016): Similar to the above SVM method, we feed
those handcrafted features to the standard XGBoost classification model for
detecting the hallucinations.

  * •

BERTbase (Devlin et al., 2019): We concatenate the headline and the article
text (with a [SEP] separator) and feed it into the pretrained BERT base model
for prediction.

  * •

T5xxl (Raffel et al., 2020): Similar to BERT, we input the concatenated
headline and article to the encoder module of T5 and use its decoder to output
one single token indicating the final predicted class.

  * •

T5xxl \+ Exp: We incorporate the natural language explanation information into
the T5xxl model by requiring its decoder to output the class token followed by
the explanation. This is similar to the reasoning classifier architecture in
our ExHalder framework.

  * •

ExHalder-NoPT: Our ExHalder framework without the NLI-based pretraining step.
Namely, we just train the explainer, the reasoning classifier, and the hinted
classifier on our news-domain hallucination detection training set.

  * •

ExHalder-NoEX: Our ExHalder framework with the NLI-based pretraining step but
without leveraging any explanation information. Namely, we force the reasoning
classifier to just output one token indicating the hallucination label and
remove the hinted classifier as well as the explainer components.

  * •

ExHalder-NoHC: Our ExHalder framework without the hinted classifier module.
Namely, we only train the explainer and the reasoning classifier during the
pretraining stage and use the reasoning classifier alone for prediction at the
inference stage.

  * •

ExHalder: The full version of our proposed framework.

We implement SVM using scikit-learn, XGBoost using its official
codebase222https://xgboost.readthedocs.io/en/stable/index.html., and BERTbase
method using the Tensorflow Model
Garden333https://github.com/tensorflow/models/tree/master/official/nlp.. For
T5xxl, T5xxl+Exp, and ExHalder along with its variants, we develop them based
on the T5X library444https://github.com/google-research/t5x. and use the
T5-11B model in the following experiments. More implementation details and
hyper-parameter settings are discussed in Appendix A.

####  5.1.3. Experiment Settings.

As we formulate the headline hallucination detection as a classification
problem, we adopt the standard classification evaluation metrics: Accuracy,
Precision, Recall, and F1 score. Among these metric, we emphasize that the
recall value indicates the percentage of hallucinated headlines captured by
the hallucination detector. Better recall means less misleading headlines will
be surfaced to users and thus leads to more positive user experiences. For
each tested method, we run it for five times and report the averaged results.
Finally, for performance comparisons, we we conduct statistical significance
test using the two-tailed paired $t$-test with 95% confidence level.

Methods | Accuracy | Precision | Recall | F1
---|---|---|---|---
SVM | 57.31 | 28.65 | 20.50 | 23.90
XGBoost | 60.19 | 42.39 | 60.67 | 49.91
BERTbase | 73.46 | 71.43 | 31.38 | 43.60
T5xxl | 82.39 | 76.29 | 66.93 | 71.29
T5xxl \+ Exp | 82.62 | 78.98 | 64.15 | 70.63
ExHalder-NoPT | 82.08 | 75.96 | 66.11 | 70.69
ExHalder-NoEX | 83.17 | 80.01∗ | 64.71 | 71.54
ExHalder-NoHC | 84.08∗ | 82.06∗ | 65.69 | 72.96∗
ExHalder | 84.46∗ | 82.63∗ | 67.16∗ | 74.08∗

Table 1. Quantitative results on the news headline hallucination detection
dataset. The superscript ∗ means the improvement is statistically significant
compared to T5xxl.

####  5.1.4. Experiment Results.

Below we first present the main experiment results and compare ExHalder with
the baseline methods. Then, we conduct ablation analysis to study how the key
components of ExHalder impact the framework overall performance. Finally, we
present a few case studies to demonstrate the potential impacts of ExHalder in
real-world scenarios.

1\. Overall Detection Performance. Table 1 presents the results of all
compared methods. First, we can see that the results of those traditional
methods with manual feature engineering (i.e., SVM, XGBoost) are
unsatisfactory. This shows that headline hallucination detection is a
challenging task and requires models to capture the subtle semantic
differences between the article and the headline. Second, we compare ExHalder
with ExHalder-NoPT and see that the NLI-based pretraining indeed helps us to
better identify the hallucinated headlines by warming up the model with
entailment task semantics. Third, by comparing ExHalder with ExHalder-NoEX, we
observe further performance improvements and this demonstrates that injecting
the explanation information into the model training process is useful.
Finally, we can see our proposed ExHalder has the overall best performance
across all the metrics and defeats the second-best method by a large margin.

Figure 4. Parameter sensitivity analysis on the news hallucination detection
dataset. We vary the number of explanations generated by the explainer
component and compute the accuracy and recall of ExHalder.

2\. Ablation Analysis of Model Components. ExHalder contains three key
components: a reasoning classifier, a hinted classifier, and an explainer. The
above ExHalder-NoEX demonstrates the importance of the reasoning classifier
component and the explainer component. Here, we study how the hinted
classifier components affect the performance of ExHalder. As shown in Table 1,
we can see that removing the hinted classifier leads to low prediction
accuracy and significantly hurts the hallucination detection recall.

3\. Explainer Augmentation Analysis. We continue to evaluate the explainer
component by directly varying its parameter $K$, namely the number of its
generated explanations used for augmenting reasoning and hinted classifier
training. As shown in Figure 4, the model performance first increases as $K$
increases until it reaches about 3 to 4 and then starts decreasing. Notably,
the performance dropping rates vary across different evaluation metrics. The
model accuracy drops faster compared to its recall. This is probably because
the quality of generated explanations will decrease if we force the explainer
to generate lots of explanations. Finally, we can see that for a wide range of
$K$, the performance of ExHalder is better than ExHalder-NoEX, which further
demonstrates the usefulness of free-text explanations.

Headline: WWE SmackDown results - 2/9/19
---
Article: _title_ : WWE Friday Night SmackDown Results (3/26/21) _passage_ :
WWE Friday Night SmackDown Results March 26, 2021 Report by Lovell Porter for
Wrestlezone.com You can also participate via social media by using the #WZChat
hashtag to voice your thoughts on tonight’s show. We want you to share our
exclusive coverage page …
Human rated class: “Contradict”
Human provided explanation: “the date in the headline different from the one
appearing in the article.”
ExHalder output: “Contradict because conflicting dates - 2021 vs 2019.”
Headline: SpaceX delays launch of Starlink satellites
Article: _title_ : This Could Be a Big Reason Why SpaceX Is Delaying Its
Starlink IPO _passage_ : here is a lot of interest in investing in SpaceX, but
an IPO making the company available to everyday investors is likely still a
long way off. CEO Elon Musk has said SpaceX internet service provider
subsidiary Starlink is much closer to …
Human rated class: “Entail”
Human provided explanation: “” (i.e., rater does not give any explanation)
ExHalder output: “Contradict because IPO is missing in the headline which
makes it misleading."
Headline: Lockdown at 16 hotspots in Thane till March 31
Article: _title_ : Maharashtra: Thane Municipal Corporation does U-turn on
lockdown in 16 hotspots _passage_ : HANE: The Thane Municipal Corporation
(TMC) in Maharashtra, on Tuesday evening, did a complete U-turn on its Monday
announcement of lockdown in 16 hotspots in the district. The TMC issued fresh
guidelines tapering restrictions to the respective floors of a building or
wings depending on the caseload, till March end, officials said….
Human rated class: “Entail”
Human provided explanation: “” (i.e., rater does not give any explanation)
ExHalder output explanation: “Contradict because till March 31 is
unsupported.”

Table 2. ExHalder output case studies. We use two colors to highlight the key
information in headlines and articles.

Headline: Kyle takes his own life in Hollyoaks
---
Article: _title_ : Does Kyle die in Hollyoaks? _passage_ : Hollyoaks will
tackle the subject of male suicide this week though a hard-hitting storyline
featuring Kyle Kelly (Adam Rickitt). … However, last week, conflict ensued
between the friends after Kyle caused a devastating car crash while on drugs …
Human rated class: “Contradict”
Human provided explanation: “” (i.e., rater does not give any explanation)
ExHalder explainer output 1: “Kyle is in a car crash so he doesn’t take his
own life.”
ExHalder explainer output 2: “Kyle is in a car crash, not taking his own
life.”
ExHalder explainer output 3: “Kyle does not die in Hollyoaks. The show is
about male suicide.”

Table 3. Case study of the explainer module in our ExHalder framework.

4\. Case Studies. Table 2 shows some ExHalder output examples. More case
studies are presented in Appendix C. First, we observe that ExHalder can
generate high-quality human-readable explanations to justify its prediction.
In the first example, the model output explanation “conflicting dates - 2021
vs 2019.” captures the key difference between the headline and the article,
and closely resembles the human written explanation “the date in the headline
different from the one appearing in the article”.

Second, we can see that ExHalder is able to help us identify potential
labeling errors. Take the second example as one case, the rater mistakenly
labels it as an "Entail" case but in fact it should be misleading because the
headline suggests the Starlink satellites launch is delayed but the article is
about the delay of Starlink IPO. We can capture this error based on the model
output explanation “IPO is missing in the headline which makes it misleading”.

Moreover, we can see that the generated explanation enables us to understand
why the model makes a certain mistake. As shown by the last example in Table
2, the headline is indeed supported by the news article but our ExHalder
predicts it to be a contradiction case because the “till March 31” is not
supported by the article. Diving into the news article, we can see the “till
March 31” information is referred to as “till March end” in the middle part of
the article’s main passage. The model fails to recognize that the “March end”
is synonymous with “March 31” and thus makes the wrong prediction. This
observation can motivate researchers to later study how to further increase
the hallucination accuracy by improving the model’s temporal reasoning
ability.

Finally, Table 3 shows one example where the original curator does not provide
any rating explanations other than the binary class label while our ExHalder
explainer component can successfully generate valid explanations for the rated
class.

###  5.2. Zero-shot Hallucination Detection

Dataset: QAGS
---
Headline: The number of illegal immigrants detained or arrested at the uk’s
busiest port has more than doubled in a year, figures show.
Article: There has been a large increase in the number of illegal immigrants
detained or arrested by police at dover. Some 563 were held or arrested by
port of dover police officers last year - up from 148 in 2013, a bbc radio
kent freedom of information request shows. …
Human rated class: “Contradict”
ExHalder output: “Contradict because The number of illegal immigrants detained
or arrested at dover \- one of the uk’s busiest ports \- has more than
doubled, from 148 to 563.”
Dataset: Vitamin-C
Headline: Averaged over more than 20 critics , On the Basis of Sex scored less
than 62 % .
Article: On Metacritic , the film has a weighted average score of 61 out of
100 , based on 21 critics , indicating “generally favorable reviews” .
Human rated class: “Entail”
ExHalder output: “Entail because The film scored 61 out of 100 , which is less
than 62 % .”

Table 4. ExHalder output case studies on TRUE benchmark datasets. If the
article and the headline is contradictory, we use two different colors to
highlight the key differences. Otherwise, we use one single color to
underscore the shared key information.

####  5.2.1. Datasets.

We further evaluate the zero-shot performance of ExHalder when no in-domain
training data is provided. Specifically, we adopt the four summarization
hallucination detection datasets: MNBM (Maynez et al., 2020), FRANK (Pagnoni
et al., 2021), QAGS (Wang et al., 2020), SummEval (Fabbri et al., 2021) and
two fact verification datasets: FEVER (Thorne et al., 2018), Vitamin-C
(Schuster et al., 2021) in the TRUE benchmark (Honovich et al., 2022). Each
dataset contains a set of $\langle$target text, grounding text, hallucination
label$\rangle$ triplets where the binary label indicates if the target text is
hallucinated based on the grounding text. In the following experiments, we
treat the target text as the “headline” and the grounding text as the
“article”. More dataset details are available in Appendix B.

Datasets | Q2 | ANLI | ExHalder-NoEX | ExHalder
---|---|---|---|---
MNBM (Maynez et al., 2020) | 66.5 | 66.7 | 73.8 | 75.4
FRANK (Pagnoni et al., 2021) | 82.9 | 83.5 | 81.3 | 83.3
QAGS (Wang et al., 2020) | 78.3 | 75.3 | 76.6 | 78.4
SummEval (Fabbri et al., 2021) | 77.3 | 72.9 | 85.6 | 87.0
FEVER (Thorne et al., 2018) | 82.7 | 90.2 | 87.4 | 88.3
Vitamin-C (Schuster et al., 2021) | 75.7 | 74.7 | 84.8 | 85.1
Average | 77.23 | 77.21 | 81.58 | 82.91

Table 5. Accuracy results on the TRUE datasets (Honovich et al., 2022).

####  5.2.2. Compared Methods.

We compare our ExHalder framework and its variant ExHalder-NoEX with two best-
performing methods in the original TRUE paper: (1) ANLI which, similar to our
approach, first trains a T5-11B model using the ANLI dataset (Nie et al.,
2020) and then directly applies the learned model to detect the
hallucinations, and (2) Q2 (Honovich et al., 2021) which first uses a question
generation module to generate questions with answer spans from the target text
and then applies a question answering (QA) model on the grounding text to
answer the above-generated questions. Finally, it computes the overlap between
each true answer span and its corresponding QA model output answer span and
outputs the final hallucination score.

####  5.2.3. Experiment Settings.

As no training example is provided in the TRUE benchmark, we reuse the
ExHalder checkpoint after the NLI-based pretraining step and directly conduct
the inference step of ExHalder on all tested datasets. For fair comparisons,
we follow the previous practices (Honovich et al., 2022) to directly tune the
binary cutoff threshold on the development set and report the best performance
(in terms of accuracy) of all baseline methods in the original paper.

####  5.2.4. Experiment Results.

Table 5 shows the overall results on all six evaluated datasets. We can see
that both ExHalder and ExHalder-NoEX can outperform the previous best methods
and our ExHalder framework achieves new state-of-the-art results. Moreover, by
comparing ExHalder with ExHalder-NoEX, we observe that adding explanation
information is particularly useful in the zero-shot transfer learning setting.
We also demonstrate that ExHalder can augment the TRUE benchmark by providing
interesting and insightful free-text explanations for the existing labels. As
shown in Table 4, ExHalder generates high quality human-readable explanations
to explain its prediction results. Take the case from the QAGS dataset as an
example, ExHalder’s output explanation captures the subtle semantic difference
between “ _uk’s busiest port_ ” and “ _dover, one of the uk’s busiest ports_ ”
and justifies why it makes the “Contradict” prediction. Similarly, in the
example from Vitamin-C dataset, ExHalder reiterates the fact that “ _scored 61
out of 100_ ” (from the news article) implies “ _less than 62%_ ” (mentioned
in the headline) and thus the headline is supported by the article. More case
studies are presented in Appendix D.

##  6\. Conclusions and Future Work

This paper studies how to automatically detect news headline hallucinations
with a limited amount of labeled data. We propose a novel ExHalder framework
which adapts knowledge from public NLI datasets into the news domain and
generates natural language explanations to justify its prediction results.
Extensive experiments on one newly collected dataset and six public datasets
demonstrate that ExHalder can accurately identify hallucinated news headlines
along with high-quality human-readable explanations.

As a first-punch solution for detecting news headline hallucinations, we
believe ExHalder can be improved in many ways. Interesting future directions
include: (1) utilizing the validation set to learn a better combiner that
better aggregates the predictions results from the reasoning classifier and
the hinted classifier, (2) incorporating large language models (e.g., GPT-3,
PaLM, ChatGPT) into ExHalder for better zero- and few-shot performance, (3)
expanding the scope of ExHalder to the multilingual setting for detecting
international news headline hallucinations, (4) formatting the ExHalder output
explanations to increase their readability, (5) enforcing the ExHalder output
explanation itself to be entailed by the original news article and headline,
and (6) extending ExHalder to resolve multi-document headline hallucination
problems where the headline is generated from multiple documents and we need
to predict if it is hallucinated based on a whole set of documents.

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##  Appendix A Experiment Details on News Hallucination Detection Dataset

For all compared methods, we tune their hyper-parameters using the validation
set, select the best ones, and report the corresponding results on the test
set. Specifically, we have: for SVM555https://scikit-
learn.org/stable/modules/generated/sklearn.svm.SVC.html, we use the RBF kernel
with C=0.1 and degree=4; for
XGBoost666https://xgboost.readthedocs.io/en/stable/index.html, we select
gamma=1.0, max_depth=3, min_child_weight=1, subsample=1.0,
colsample_bytree=0.5, and n_estimators=30; for
BERTbase777https://github.com/tensorflow/models/tree/master/official/nlp and
T5xxl888https://github.com/google-research/t5x methods, we select
batch_size=64 and learning_rate=1e-3. Both methods use a constant learning
rate scheduler and are trained for 10k steps with 1k warmup steps. For our
ExHalder framework and its variants, during the NLI-based pretraining stage,
we choose batch_size=128, constant learning_rate=1e-3, and the number of
explainer generated explanations $K=1$. During the domain fine-tuning stage,
we select batch_size=64, constant learning_rate=1e-3, and the number of
explainer generated explanations $K=3$. Both stages are trained for 10k steps
with 1k warmup steps. Finally, we train BERTbase, T5xxl, and our models on TPU
v3.

##  Appendix B TRUE Benchmark Datasets Statistics

Table 6 lists the statistics of TRUE benchmark datasets.

Datasets | # of Examples
---|---
MNBM (Maynez et al., 2020) | 2,500
FRANK (Pagnoni et al., 2021) | 671
QAGS (Wang et al., 2020) | 239
SummEval (Fabbri et al., 2021) | 1,600
FEVER (Thorne et al., 2018) | 18,209
Vitamin-C (Schuster et al., 2021) | 63,054

Table 6. Statistics of TRUE Benchmark Datasets.

##  Appendix C Case Studies on News Hallucination Detection Dataset

Table 7 lists case studies on our news hallucination detection dataset.

Headline: WWE SmackDown results - 2/9/19
---
Article: _title_ : WWE Friday Night SmackDown Results (3/26/21) _passage_ :
WWE Friday Night SmackDown Results March 26, 2021 Report by Lovell Porter for
Wrestlezone.com You can also participate via social media by using the #WZChat
hashtag to voice your thoughts on tonight’s show. We want you to share our
exclusive coverage page …
Human rated class: “Contradict”
Human provided explanation: “the date in the headline different from the one
appearing in the article.”
ExHalder output: “Contradict because conflicting dates - 2021 vs 2019.”
Headline: SpaceX delays launch of Starlink satellites
Article: _title_ : This Could Be a Big Reason Why SpaceX Is Delaying Its
Starlink IPO _passage_ : here is a lot of interest in investing in SpaceX, but
an IPO making the company available to everyday investors is likely still a
long way off. CEO Elon Musk has said SpaceX internet service provider
subsidiary Starlink is much closer to …
Human rated class: “Entail”
Human provided explanation: “” (i.e., rater does not give any explanation)
ExHalder output: “Contradict because IPO is missing in the headline which
makes it misleading."
Headline: OnePlus 8T Cyberpunk 2077 Edition launched
Article: _title_ : OnePlus 8T Cyberpunk 2077 Edition unveiled _passage_ :
OnePlus has unveiled a new special edition smartphone, the OnePlus 8T
Cyberpunk 2077 Edition and the handset is now available to pre-order in China.
The new OnePlus 8T Cyberpunk 2077 Edition …
Human rated class: “Entail”
Human provided explanation: “” (i.e., rater does not give any explanation)
ExHalder output: “Contradict because launched VS unveiled.”
Headline: Lockdown at 16 hotspots in Thane till March 31
Article: _title_ : Maharashtra: Thane Municipal Corporation does U-turn on
lockdown in 16 hotspots _passage_ : HANE: The Thane Municipal Corporation
(TMC) in Maharashtra, on Tuesday evening, did a complete U-turn on its Monday
announcement of lockdown in 16 hotspots in the district. The TMC issued fresh
guidelines tapering restrictions to the respective floors of a building or
wings depending on the caseload, till March end, officials said….
Human rated class: “Entail”
Human provided explanation: “” (i.e., rater does not give any explanation)
ExHalder output explanation: “Contradict because till March 31 is
unsupported.”

Table 7. ExHalder output case studies. We use two colors to highlight the key
information in headlines and articles.

##  Appendix D Case Studies on TRUE Benchmark Datasets

Table 8 lists case studies on TRUE benchmark datasets.

Dataset: MNBM
---
Headline: singer rod temperton, who wrote the hit album michael jackson, has
died at the age of 89.
Article: Temperton died in London last week at the age of 66 after “a brief
aggressive battle with cancer”, Jon Platt of Warner/Chappell music publishing
said. Temperton’s other hits included Off The Wall and Baby Be Mine for
Jackson and Boogie Nights for his band Heatwave. Chic guitarist Nile Rodgers
was among those paying tribute, tweeting: …
Human rated class: “Contradict”
ExHalder output: “Contradict because he did die at the age of 66, not 89.”
Dataset: FEVER
Headline: Heroes had a season with over 20 episodes.
Article: Heroes is an American psychological thriller science fiction
television drama series created by Tim Kring that appeared on NBC for four
seasons from September 25 , 2006 through February 8 , 2010 . The critically
acclaimed first season had a run of 23 episodes and garnered an average of
14.3 million viewers in the United States , …
Human rated class: “Entail”
ExHalder output: “Entail because The first season had a run of 23 episodes.”

Table 8. ExHalder output case studies on TRUE benchmark datasets. If the
article and the headline is contradictory, we use two different colors to
highlight the key differences. Otherwise, we use one single color to
underscore the shared key information.