# Assessing the Effectiveness of GPT-3 in Detecting False Political Statements: A Case Study on the LIAR Dataset

arXiv: https://arxiv.org/abs/2306.08190 · HTML: https://mlsanity.com/paper/2306.08190/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape](https://mlsanity.com/paper/1803.10124/) (2018)

## Built on by (most cited first, 7 of 7)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions](https://mlsanity.com/paper/2503.22353/) (2025)
- [CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking](https://mlsanity.com/paper/2408.08535/) (2024)
- [Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning](https://mlsanity.com/paper/2510.01932/) (2025)
- [MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation](https://mlsanity.com/paper/2403.14171/) (2024)
- [Identifying the sources of ideological bias in GPT models through linguistic variation in output](https://mlsanity.com/paper/2409.06043/) (2024)

## Abstract (arXiv)

The detection of political fake statements is crucial for maintaining information integrity and preventing the spread of misinformation in society. Historically, state-of-the-art machine learning models employed various methods for detecting deceptive statements. These methods include the use of metadata (W. Wang et al., 2018), n-grams analysis (Singh et al., 2021), and linguistic (Wu et al., 2022) and stylometric (Islam et al., 2020) features. Recent advancements in large language models, such as GPT-3 (Brown et al., 2020) have achieved state-of-the-art performance on a wide range of tasks. In this study, we conducted experiments with GPT-3 on the LIAR dataset (W. Wang et al., 2018) and achieved higher accuracy than state-of-the-art models without using any additional meta or linguistic features. Additionally, we experimented with zero-shot learning using a carefully designed prompt and achieved near state-of-the-art performance. An advantage of this approach is that the model provided evidence for its decision, which adds transparency to the model's decision-making and offers a chance for users to verify the validity of the evidence provided.