Summary Bias in Fake News Detection of LLMs arxiv.org
7,563 words - PDF document - View PDF document
One Line
Fake news detectors often misclassify content generated by language model models (LLM) as fake, but detection accuracy can be improved through the use of adversarial training and datasets.
Slides
Slide Presentation (9 slides)
Key Points
- Fake news detectors are biased against texts generated by Large Language Models (LLMs).
- Existing detectors are more likely to flag LLM-generated content as fake news while misclassifying human-written fake news as genuine.
- The bias is due to distinct linguistic patterns inherent to LLM outputs.
- Adversarial training with LLM-paraphrased genuine news can mitigate this bias and improve detection accuracy.
- Researchers have released two comprehensive datasets, GossipCop++ and PolitiFact++, for further research in developing and evaluating fake news detectors.
Summaries
20 word summary
Fake news detectors are biased against LLM-generated content, misclassifying it as fake. Adversarial training and datasets can improve detection accuracy.
62 word summary
A study found that fake news detectors are biased against texts generated by Large Language Models (LLMs), flagging LLM-generated content as fake while misclassifying human-written fake news. Researchers proposed using adversarial training with LLM-paraphrased genuine news to address this bias, improving detection accuracy. They also released datasets for further research and highlighted the challenge of fake news and the impact of LLMs.
155 word summary
Fake news detectors have been found to be biased against texts generated by Large Language Models (LLMs), according to a study conducted by Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, and Preslav Nakov. The detectors were more likely to flag LLM-generated content as fake news while misclassifying human-written fake news as genuine. To address this bias, the researchers proposed a mitigation strategy using adversarial training with LLM-paraphrased genuine news, which improved detection accuracy for both human and LLM-generated news. The researchers released two comprehensive datasets, GossipCop++ and PolitiFact++, to facilitate further research. The study emphasized the challenge of fake news and the impact of LLMs in generating believable content. The researchers introduced a new setting for evaluating detectors that includes both human-written and LLM-generated fake news. The analysis revealed a bias towards LLM-generated fake news, and the researchers proposed a debiasing technique using adversarial training with LLM-paraphrased real news to mitigate this bias.
472 word summary
Fake News Detectors have been found to be biased against texts generated by Large Language Models (LLMs), according to a study conducted by Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, and Preslav Nakov. The study aimed to evaluate the performance of fake news detectors in scenarios involving both human-written and LLM-generated misinformation. The findings revealed a significant bias in many existing detectors, as they were more likely to flag LLM-generated content as fake news while misclassifying human-written fake news as genuine. This bias appeared to be due to distinct linguistic patterns inherent to LLM outputs.
To address this bias, the researchers proposed a mitigation strategy that leverages adversarial training with LLM-paraphrased genuine news. This approach improved the detection accuracy for both human and LLM-generated news. In order to facilitate further research in this domain, the researchers released two comprehensive datasets, GossipCop++ and PolitiFact++, which contain human-validated articles along with LLM-generated fake and real news.
The study highlighted the critical challenge of fake news, which undermines trust and poses threats to society. The emergence of LLMs has intensified these concerns, as they have the capability to generate believable fake content at an unprecedented scale. Adversaries are increasingly using LLMs to automate fake news curation, resulting in a surge in the amount of fake news. The researchers emphasized the need to study how LLMs affect fake news detection, particularly the detection of LLM-generated fake news.
The researchers introduced a new and realistic setting for evaluating fake news detectors, where detectors must identify both human-written and LLM-generated fake news. This reflects real-world situations more accurately, considering the increasing usage of LLMs in disseminating disinformation. Testing detectors against human and LLM-generated content allows for the assessment of their resilience and effectiveness in an evolving fake news landscape.
The analysis of various fake news detectors revealed that they demonstrated a bias towards LLM-generated fake news, even when it was truthful. The detectors performed better in detecting LLM-generated fake news compared to human-written fake news, contrary to previous concerns about the challenges of identifying LLM-generated fake news. The researchers paraphrased human-written real news using ChatGPT and found that the detectors performed much worse on LLM-paraphrased real news than human-written ones. This bias towards LLM-generated texts led to misclassification of LLM-generated real news as fake news.
To mitigate this bias, the researchers investigated whether detectors took 'shortcuts' to learn LLM-generated fake news. They analyzed content-based features of news articles and proposed a debiasing technique that leveraged adversarial training with LLM-paraphrased real news. This strategy effectively reduced biases and improved the performance of fake news detectors on both human-written and LLM-generated content.
In conclusion, the study revealed a significant bias in fake news detectors towards LLM-generated content. The researchers proposed a mitigation strategy that improved detection accuracy and released two comprehensive datasets for further research in this domain.
506 word summary
Fake News Detectors are biased against texts generated by Large Language Models (LLMs), according to a study conducted by Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, and Preslav Nakov. The study aims to evaluate the performance of fake news detectors in scenarios involving both human-written and LLM-generated misinformation. The findings reveal a significant bias in many existing detectors, as they are more likely to flag LLM-generated content as fake news while misclassifying human-written fake news as genuine. This bias appears to be due to distinct linguistic patterns inherent to LLM outputs.
To address this bias, the researchers propose a mitigation strategy that leverages adversarial training with LLM-paraphrased genuine news. This approach improves the detection accuracy for both human and LLM-generated news. To facilitate further research in this domain, the researchers release two comprehensive datasets, GossipCop++ and PolitiFact++, which contain human-validated articles along with LLM-generated fake and real news.
The study begins by highlighting the critical challenge of fake news, which undermines trust and poses threats to society. The emergence of LLMs has intensified these concerns, as they have the capability to generate believable fake content at an unprecedented scale. Adversaries are increasingly using LLMs to automate fake news curation, resulting in a surge in the amount of fake news. The researchers emphasize the need to study how LLMs affect fake news detection, particularly the detection of LLM-generated fake news.
The researchers introduce a new and realistic setting for evaluating fake news detectors, where detectors must identify both human-written and LLM-generated fake news. This reflects real-world situations more accurately, considering the increasing usage of LLMs in disseminating disinformation. Testing detectors against human and LLM-generated content allows for the assessment of their resilience and effectiveness in an evolving fake news landscape.
The analysis of various fake news detectors reveals that they demonstrate a bias towards LLM-generated fake news, even when it is truthful. The detectors perform better in detecting LLM-generated fake news compared to human-written fake news, contrary to previous concerns about the challenges of identifying LLM-generated fake news. The researchers paraphrase human-written real news using ChatGPT and find that the detectors perform much worse on LLM-paraphrased real news than human-written ones. This bias towards LLM-generated texts leads to misclassification of LLM-generated real news as fake news.
To mitigate this bias, the researchers investigate whether detectors take 'shortcuts' to learn LLM-generated fake news. They analyze content-based features of news articles and propose a debiasing technique that leverages adversarial training with LLM-paraphrased real news. This strategy effectively reduces biases and improves the performance of fake news detectors on both human-written and LLM-generated content.
The researchers provide two new datasets, GossipCop++ and PolitiFact++, which contain human-validated articles along with LLM-generated fake and real news. These datasets serve as benchmarks and valuable resources for further research into developing and evaluating fake news detectors.
In conclusion, the study reveals a significant bias in fake news detectors towards LLM-generated content. The researchers propose a mitigation strategy that improves detection accuracy and release two comprehensive datasets for further research in this domain.