# Pitfalls of Conversational LLMs on News Debiasing

###### Abstract

This paper addresses debiasing in news editing and evaluates the effectiveness
of conversational Large Language Models in this task. We designed an
evaluation checklist tailored to news editors’ perspectives, obtained
generated texts from three popular conversational models using a subset of a
publicly available dataset in media bias, and evaluated the texts according to
the designed checklist. Furthermore, we examined the models as evaluator for
checking the quality of debiased model outputs. Our findings indicate that
none of the LLMs are perfect in debiasing. Notably, some models, including
ChatGPT, introduced unnecessary changes that may impact the author’s style and
create misinformation. Lastly, we show that the models do not perform as
proficiently as domain experts in evaluating the quality of debiased outputs.

Keywords: News Bias Correction, LLMs, Human Evaluation, Automatic Evaluation

Pitfalls of Conversational LLMs on News Debiasing

Ipek Baris Schlicht1,2, Defne Altiok1, Maryanne Taouk3, Lucie Flek4,5,6
---
1Deutsche Welle, Bonn/Berlin, Germany
2Universitat Politecnica de Valencia, Spain
3ABC News, Australia
4Conversational AI and Social Analytics (CAISA) Lab
5Bonn-Aachen International Center for Information Technology (b-it),
University of Bonn
6The Lamarr Institute for Machine Learning and Artificial Intelligence

Abstract content

## 1\. Introduction

Biased news articles have the potential to significantly shape public opinion
and discourse on various issues. Thus, professional news editors identify bias
text spans in news articles before they are published. This task is
particularly challenging, especially when editorial teams face constraints
such as time-pressure and a lack of human resources.

Large Language Models (LLMs) have demonstrated outstanding performance even in
the absence of labeled data, through zero-shot prompting. In many tasks, LLMs
have surpassed the performance of the supervised models and have even employed
as writing assistance Shi et al. (2022); Zhang et al. (2023). In addition,
conversational LLMs such as ChatGPT OpenAI (2022) and GPT4 OpenAI (2023) are
user-friendly, making them accessible to non-technical experts like
journalists who can use them without coding knowledge to aid in their tasks.
As a result, many media companies have already begun experimenting ChatGPT for
various journalistic tasks Beckett (2023). Limited studies have explored
debiasing through text generation with conversational LLMs for the tasks such
as hate speech Plaza-del arco et al. (2023) and toxicity detectionMorabito et
al. (2023). These studies explored zero-shot prompting with conversational
LLMs. To the best of our knowledge, conversational LLMs have not been explored
for news debiasing.

President Donald Trump gave states and local governments the right to reject
refugees, but instead of saying no, most state and local officials have
blindsided the administration by opting in, according to two former officials
familiar with the matter.
---
President Donald Trump allowed states and local governments the option to
refuse refugees. However, according to two former officials familiar with the
matter, most state and local officials have chosen to accept refugees.
Figure 1: Biased text where the usage “blind-sided” introduces bias by
conveying a strong negative opinion about the actions of state and local
officials and its GPT4 debiased version which doesn’t contain toxicity
according to Perspective API. Debiasing changed the facts and the context
(factually incorrect statement highlighted in red, original version in blue).

ID Concept Question C1 Correcting Bias Does the model produce unbiased text?
Grade 1-3 The text is unbiased. (3) The text is partially biased. (2) The text
is highly biased. (1) C2 Preserving Information Does the model change textual
facts? Grade 1-3 The text facts are still present. (3) Some facts are missing.
(2) Facts are completely missing. (1) C3 Preserving Context Does the model
change the meaning of text? Grade 1-3 The meaning of the text is preserved.(3)
The meaning of the text is partially preserved. (2) The meaning of the text is
completely changed.(1) C4 Preserving Language Fluency Does the model produce
grammatically correct text? Grade 1-3 The text is grammatically correct. (3)
The text has few grammar issues. (2) The text has many grammar issues. (1) C5
Preserving Author’s Style Does the model harm the author’s creativity? Grade
1-3 No, the model did all necessary changes without harming author creativity.
(3) The model corrected some of the texts that might hurt the creativity. (2)
The model did unnecessary changes, and changed the text style. (1)

Table 1: News editorial criteria for checking quality of debiasing.

Standard evaluation metrics Min et al. (2023) such as ROUGE require a
reference text for measuring generated text quality and lacks explanatory
evaluation. Morabito et al. (2023) established an evaluation protocol for
automatically assessing LLMs’ consistency in debiasing for toxicity detection
by using Perspective APIper  as the evaluator. However, this protocol is
limited to bias reduction and may not be suitable for the news domain. In the
context of news bias, bias encompasses both overt bias such derogatory terms
within text and latent biases that shape the language and framing of news
stories Recasens et al. (2013). As shown in Figure 1, news texts deemed biased
may not contain toxicity but wording/phrasing could introduce bias. Hence,
tools such as Perspective API could fail to quantify bias reduction.
Furthermore, the debiased text might produce misinformation by changing
context and factuality and altering the author’s writing style. Therefore,
there is a need for evaluation criteria discerned editorial perspectives.

To address these issues, we investigate the following research questions
(RQs): (1) How well do conversational LLMs perform debiasing in the context of
the news domain according to editorial criteria? (2) Can conversational LLMs
also serve as an evaluation tool for assessing the editorial quality of
debiased articles?

Given the need for a domain-specific evaluation to assess the quality of
conversational LLMs in news debiasing, we propose a set of evaluation criteria
tailored to news editors. Since there is no publicly available news dataset
for debiasing, we obtained text generations on a subset of the publicly
available bias classification dataset using three popular conversational LLMs
and a fine-tuned T5 Raffel et al. (2020). Expert news editors from
international media organizations ranked the models’ outputs based on the
editorial criteria. Additionally, we compared model outputs with expert
assessments when the models were used as evaluation tools to check the quality
of debiasing. Our results showed that despite conversational LLMs’ proficiency
in bias reduction, they sometimes generate misinformation and alter writing
styles. Moreover, they can not assess debiased outputs as the experts do
111The code and the data are at https://bit.ly/3vGphbw.

## 2\. Related Works

The studies on media bias have primarily focused on two aspects: identifying
biased text spans Spinde et al. (2021); Hamborg (2020); Lei et al. (2022) and
detecting political bias in news articles Chen et al. (2020) or media outlets
Baly et al. (2020). Only a few studies proposed methods for mitigating bias
through article generation using transformer models. Among these studies, the
earliest work by Pryzant et al. (2020) used BERT to identify subjective
content and update the hidden layers of the model to generate unbiased text
from Wikipedia. Lee et al. (2022) applied a summarization method on articles
from various political leanings to neutralize news.

Plaza-del arco et al. (2023) and Morabito et al. (2023) explored the potential
of zero-shot prompting with LLMs, respectively for hate speech detection and
reducing toxicity in user comments. Additionally, Morabito et al. (2023)
established an evaluation protocol for evaluating consistency of LLMs on
debiasing in the context of toxicity detection. The authors used Perspective
API as the evaluator tool which provides toxicity scores for comment
moderation. However, the protocol is limited to only to bias reduction.
Furthermore, is not applicable within the news domain as news articles may not
exhibit a toxic tone, yet they can still contain biases favoring certain
groups, which need to be addressed before publication. In our work, we design
evaluation criteria taking into account journalistic perspective to measure
quality of debiased sentences.

Recently, researchers have explored LLMs as evaluators for assessing the
quality of text generation in various applications Gao et al. (2024); Min et
al. (2023) as an alternative solution to costly expert assessments. Motivated
by this, we evaluate the conversational LLMs models as evaluators for
assessing the quality of debiased sentences based on the journalistic criteria
and compare them with our expert evaluation.

## 3\. Methodology

We investigated three conversational LLMs for debiasing news sentences and
paragraphs. Given sentences or paragraphs containing bias types such as
epistemological, framing and demographic bias Pryzant et al. (2020); Spinde et
al. (2021); Recasens et al. (2013), the goal of the task was to generate an
unbiased version of those sentences. The outcome of the sentences should be
unbiased but other criteria should also be considered as important for news
editors, such as preserving factuality, news’ message, and not harming the
authors’ creativity, along with grammar changes.

### 3.1. News Editorial Criteria

As prior evaluation metrics are limited to news debiasing, we propose news
editorial criteria. The editorial criteria were created during the
implementation of BiasBlocker, which is a prototype AI-based news editor.
222https://bit.ly/4aJttWD.

The BiasBlocker team comprises experienced news editors and technologists from
Deutsche Welle, ABC News and ARIJ. Since bias is a broad concept, to establish
a common ground on the bias definitions and the corrections, the editors in
the team created a codebook on bias types 333https://bit.ly/49qcnvZ and
guidelines for debiasing based on the prior studies Pryzant et al. (2020);
Spinde et al. (2021); Recasens et al. (2013) and UN Guidelines
444https://bit.ly/3PRks67. Hence, the bias types we focus on are primarily
framing, epistemological, and demographic bias.

We applied a pilot study on bias correction by using ChatGPT with the editors
555https://bit.ly/43OMCnQ. The editors spotted the issues and refined the
expectations for the news editor. As outlined in Table 1, we distilled these
expectations into five criteria for assessing the quality of models in the
context of debiasing for news editing.

Essentially, the editors expected the model to effectively remove any text
spans that introduce bias into the content. However, they also had the
expectation that this must refrain from adding new facts or removing vital
information, as this could produce misinformation. Furthermore, the model must
ensure that the meaning of the text remains intact. The debiased text must
also be grammatically correct. Lastly, especially for those articles of
opinion pieces or analyses, the model must respect and preserve the author’s
writing style and creativity. Otherwise, the model could discourage less
experienced authors and harm the communication of the news message.

Evaluation Dataset. Wiki Neutrality Corpus (WNC) (Pryzant et al., 2020) is the
only publicly available dataset that contains biased samples and their
debiased version by Wikipedia editors. Given that our research objective was
to assess the LLMs in correcting bias within texts authored by news authors,
WNC samples were not suitable for the evaluations. Therefore, we preferred the
BABE dataset Spinde et al. (2021) as the test set. BABE consists of sentences
from news articles published by US publishers with different political
leanings. Experienced media experts annotated the dataset; the dataset samples
were labeled as biased or unbiased. The authors of the dataset provided two
subsets. We chose the one annotated with more experts and randomly selected 50
biased sentences from this subset for the evaluations.

### 3.2. Debiasing Models

Baseline. As the baseline, we used the large version of T5 Raffel et al.
(2020). T5 is an encoder-decoder transformer that is pre-trained on a cleaned
Common Crawl collection, incorporating a mixture of supervised tasks through
multi-task learning. To adopt T5-large for the debiasing task, we used WNC as
the training dataset. Given our constraints with computational resources, we
fine-tuned the model using LoRA adaptation Hu et al. (2022).

Debiasing Prompt
---
Transform the following biased sentence into an unbiased sentence from a news
article by removing any subjective language or discriminatory undertones
without changing its semantic meaning:
Biased Sentence:
{{sentence}}
Unbiased Sentence:
Evaluator Prompt (shortened)
The input sentence from a news article is biased, it uses subjective language
or discriminatory undertones. The other sentence was debiased by a language
model. Your task is to compare two sentences based on the following
journalistic criteria. For each question in the checklist, select your
response from {1, 2, 3}.
The checklist is as follows:
1- Does the model produce unbiased text?
\- If the text is unbiased, return 3
…
Do not explain your decisions.
Biased Sentence:
{{sentence}}
Model Output:
{{model_output}}
Checklist Answers:
Figure 2: Prompts for debiasing and evaluation. The full version of the
evaluator prompt can be found at our source code.

Conversational LLMs. We evaluated the popular conversational LLMs: ChatGPT
OpenAI (2022) and GPT4 OpenAI (2023) from OpenAI, and Llama2-70b-chat Touvron
et al. (2023) which is an open source, popular alternative to ChatGPT. The
models were adopted for conversational tasks using reinforcement learning with
human feedback. In this way, individuals without technical expertise could
easily interact with the models, making them suitable for integration into
news organizations.

ChatGPT and Llama 2 are Autoregressive Language Models trained on large
corpora from multiple sources from the web, with the objective of predicting
the next word based on the preceding context. GPT4 is the advanced version of
ChatGPT, capable of handling multi-modal input. While our task focused on
textual input, we included GPT4 in our evaluations, because human evaluators
preferred GPT4 outputs from various tasks over ChatGPT OpenAI (2023). We used
prompts, which are shown in Table 2, for each of the conversational.

## 4\. Results

ID Grade T5 Llama2 ChatGPT GPT4 C1 1 0.26 0.08 0.02 0 2 0.40 0.06 0.14 0.38 3
0.34 0.86 0.84 0.62 C2 1 0.1 0.4 0.26 0.2 2 0.12 0.44 0.36 0.56 3 0.78 0.16
0.38 0.24 C3 1 0.12 0.34 0.20 0.06 2 0.06 0.4 0.48 0.68 3 0.82 0.26 0.32 0.26
C4 1 0.34 0.1 0.02 0 2 0.2 0 0 0.12 3 0.46 0.9 0.98 0.88 C5 1 0.14 0.44 0.42
0.42 2 0.08 0.46 0.56 0.5 3 0.78 0.1 0.02 0.08

Table 2: The conversational LLMs are significantly better than the baseline at
correcting bias and providing grammatically correct outputs (Student’s T-test,
p-value at 0.05), they have issues on preserving information, context and
author’s style.

Although BABE contains the biased text spans along with the labels, the
dataset does not have the corrected versions of the biased texts. Therefore,
we could not directly apply the evaluation criteria to the samples. For this
reason, two expert news editors from the team, as described in § 3.1,
conducted the human evaluations voluntarily. Due to resource constraints, we
split the models’ results into two parts for both evaluators. Each part
contains the results from each model. One editor ranked the samples which they
were responsible for, by using a 3-likert scale. During the ranking
evaluation, the editor marked the samples they were unsure about, made notes
and applied fact-checking to address the C2 and C3. The other editor reviewed
the ranked samples while checking the notes, marked samples and the fact-
checked ones. The editors regularly engaged in discussions to reach a
consensus on disagreements and uncertain cases. In total, we obtained 200
evaluations from the experts. Table 2 presents the frequency of ratings per
criterion.

RQ1: Debiasing Performance of the Conversational Models. The conversational
LLMs proved better than the baseline for debiasing. Surprisingly, Llama 2
demonstrated comparative results even though ChatGPT has been known to
outperform others in various tasks Touvron et al. (2023). The researchers of
Llama 2’s training regime - that the factual sources were prioritized in
training samples - might account for its competitive performance in this task.
The conversational LLMs also exhibited more grammatical correctness than the
baseline. Nevertheless, some LLMs changed phrases they considered biased,
while others removed words or sentiments that could be considered
confrontational or impolite, but are not actually biased towards any
particular group. For instance, GPT4 changed ’When carrying a firearm, you
have the ultimate power of force in your control’ to ’When carrying a firearm,
you have a significant level of potential force at your disposal’.

The conversational LLMs performed worse than the baseline model in preserving
information and context. These models introduced unnecessary amendments to the
generated texts. In some cases, even created hallucinations. This issue is not
unique to this study and has been reported in related studies, especially in
the case of ChatGPT being used for various tasks Bang et al.
(2023).Additionally, the news editors observed that Llama 2 introduced
additional information not present in the input text, albeit factually
accurate. For example, in a text mentioning ’Wilkens’, the model replaced
’Wilkens’ with ’Judge Wilkens’. The model may have memorized such information
from its training dataset. This behavior by conversational models might harm
the author’s style.

ID | Llama2 | ChatGPT | GPT4
---|---|---|---
C1 | 0.0666 | -0.0489 | 0.1109
C2 | -0.0145 | 0.0285 | 0.0018
C3 | 0.1971 | 0.0280 | 0.0263
C4 | 0.0597 | 0 | 0
C5 | -0.0022 | 0.0454 | -0.0413
Table 3: The disagreement between the conversational tools as an evaluator and
the expert evaluation is high, according to Cohen’s Kappa.

RQ2: Conversational LLMs as Evaluator: We obtained rankings from the
conversational LLMs and compared them with the expert rankings. As shown in
Table 3, there is a high disagreement between the models and the expert
evaluations. Additionally, we observed that the models rated the criteria,
such as preserving factuality, grammar, with the highest score. In contrast,
the ratings by the experts for these criteria were low.

## 5\. Conclusion

Through the editorial criteria, we showed that none of the conversational LLMs
are perfect, even though they are good at debiasing. Specifically, they failed
to preserve vital information and context, often leading to hallucinations.
Employing these tools in a fully automatic editor can be dangerous, as they
can create misinformation.

Memorization also surfaces as an important aspect of LLM behavior. For this
reason, to ensure a fair evaluation of debiasing tasks across news articles
from different periods, Media bias researchers need to create benchmark
datasets containing samples from time periods that is not covered within the
training data of LLMs.

The assessments by the models are not close to those by the experts. We plan
to increase the size of our annotations and the number of annotators to build
a benchmark dataset for a fine-grained analysis of the models’ issues. We then
investigate advanced methods for automating the evaluation criteria and
incorporating them to adapt the models.

## Ethical Considerations and Limitations

In this study, we assessed the efficiency of conversational LLMs in debiasing
news articles, focusing solely on English samples from US Media. As a result,
the generalizability of our conclusions to other languages and to media in
other countries may be limited.

The dataset employed in this research paper is derived from publicly
accessible sources and is peer-reviewed. During the evaluation process, we
refrained from disclosing the identities of the article publishers to the
participating news editors, thereby preventing any potential influence on
their evaluations.

## Acknowledgement

This research was partially funded by the JournalismAI Fellowship Programme
2024 of PolisLSE and vera.ai, which is co-financed by the European Union,
Horizon Europe programme, Grant Agreement No 101070093. We also thank Kevin
Nguyen, Saja Mortada, Khalid Waleed for their support.

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