# News Verifiers Showdown: A Comparative Performance Evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and Bard in News Fact-Checking

arXiv: https://arxiv.org/abs/2306.17176 · HTML: https://mlsanity.com/paper/2306.17176/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 31 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Built on by (most cited first, 16 of 16)

- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
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- [How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models](https://mlsanity.com/paper/2407.00369/) (2024)
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- [DECEIVE-AFC: Adversarial Claim Attacks against Search-Enabled LLM-based Fact-Checking Systems](https://mlsanity.com/paper/2602.02569/) (2026)
- [Multiclass Classification of Policy Documents with Large Language Models](https://mlsanity.com/paper/2310.08167/) (2023)
- [Beyond Retrieval: Improving Evidence Quality for LLM-based Multimodal Fact-Checking](https://mlsanity.com/paper/2505.03135/) (2025)
- [Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance](https://mlsanity.com/paper/2506.18501/) (2025)
- [MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning](https://mlsanity.com/paper/2510.17590/) (2025)
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- [From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News](https://mlsanity.com/paper/2609.20838/) (2026)

## Abstract (arXiv)

This study aimed to evaluate the proficiency of prominent Large Language Models (LLMs), namely OpenAI's ChatGPT 3.5 and 4.0, Google's Bard(LaMDA), and Microsoft's Bing AI in discerning the truthfulness of news items using black box testing. A total of 100 fact-checked news items, all sourced from independent fact-checking agencies, were presented to each of these LLMs under controlled conditions. Their responses were classified into one of three categories: True, False, and Partially True/False. The effectiveness of the LLMs was gauged based on the accuracy of their classifications against the verified facts provided by the independent agencies. The results showed a moderate proficiency across all models, with an average score of 65.25 out of 100. Among the models, OpenAI's GPT-4.0 stood out with a score of 71, suggesting an edge in newer LLMs' abilities to differentiate fact from deception. However, when juxtaposed against the performance of human fact-checkers, the AI models, despite showing promise, lag in comprehending the subtleties and contexts inherent in news information. The findings highlight the potential of AI in the domain of fact-checking while underscoring the continued importance of human cognitive skills and the necessity for persistent advancements in AI capabilities. Finally, the experimental data produced from the simulation of this work is openly available on Kaggle.