Summary of Fake News Detection: Comparative Evaluation Of Bert-like Models and Large Language Models with Generative Ai-annotated Data, by Shaina Raza et al.
Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data
by Shaina Raza, Drai Paulen-Patterson, Chen Ding
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comparative evaluation of BERT-like encoder-only models and autoregressive decoder-only large language models (LLMs) for detecting fake news. The study introduces a dataset of labeled news articles verified by human experts, fine-tunes both model types on this dataset, and develops an instruction-tuned LLM approach with majority voting during inference. The analysis reveals that BERT-like models generally outperform LLMs in classification tasks, while LLMs demonstrate superior robustness against text perturbations. The results show the effectiveness of combining AI-based annotation with human oversight for fake news detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news is a big problem in today’s world. This study compares two types of artificial intelligence models to see which one is best at finding fake news. They created a special dataset of news articles that are labeled as real or fake, and then used this data to train the models. The results show that one type of model (BERT-like) does better than the other type (LLMs) when it comes to correctly identifying fake news. But the LLMs do better at handling changes in language. The study also shows that using both AI and human oversight is a great way to improve the accuracy of fake news detection. |
Keywords
» Artificial intelligence » Autoregressive » Bert » Classification » Decoder » Encoder » Inference
