# Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation

arXiv: https://arxiv.org/abs/2401.06920 · HTML: https://mlsanity.com/paper/2401.06920/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models](https://mlsanity.com/paper/2309.03883/) (2023)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [The case for 4-bit precision: k-bit Inference Scaling Laws](https://mlsanity.com/paper/2212.09720/) (2022)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
- [Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4](https://mlsanity.com/paper/2305.14928/) (2023)
- [Open, Closed, or Small Language Models for Text Classification?](https://mlsanity.com/paper/2308.10092/) (2023)
- [Explore the Potential of LLMs in Misinformation Detection: An Empirical Study](https://mlsanity.com/paper/2311.12699/) (2023)
- [Machine Learning Explanations to Prevent Overtrust in Fake News Detection](https://mlsanity.com/paper/2007.12358/) (2020)

## Built on by (most cited first, 9 of 9)

- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions](https://mlsanity.com/paper/2503.22353/) (2025)
- [Can GPT-4 Models Detect Misleading Visualizations?](https://mlsanity.com/paper/2408.12617/) (2024)
- [Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open LLMs for Automated Essay Scoring](https://mlsanity.com/paper/2503.11827/) (2025)
- [Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search](https://mlsanity.com/paper/2502.15990/) (2025)
- [Are LLMs More Skeptical of Entertainment News?](https://mlsanity.com/paper/2605.01727/) (2026)
- [From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News](https://mlsanity.com/paper/2609.20838/) (2026)

## Abstract (arXiv)

Recent large language models (LLMs) have been shown to be effective for misinformation detection. However, the choice of LLMs for experiments varies widely, leading to uncertain conclusions. In particular, GPT-4 is known to be strong in this domain, but it is closed source, potentially expensive, and can show instability between different versions. Meanwhile, alternative LLMs have given mixed results. In this work, we show that Zephyr-7b presents a consistently viable alternative, overcoming key limitations of commonly used approaches like Llama-2 and GPT-3.5. This provides the research community with a solid open-source option and shows open-source models are gradually catching up on this task. We then highlight how GPT-3.5 exhibits unstable performance, such that this very widely used model could provide misleading results in misinformation detection. Finally, we validate new tools including approaches to structured output and the latest version of GPT-4 (Turbo), showing they do not compromise performance, thus unlocking them for future research and potentially enabling more complex pipelines for misinformation mitigation.