Summary of Large Language Model Agent For Fake News Detection, by Xinyi Li et al.
Large Language Model Agent for Fake News Detection
by Xinyi Li, Yongfeng Zhang, Edward C. Malthouse
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 introduces FactAgent, an agentic approach to fake news detection using pre-trained large language models (LLMs). Instead of relying on direct prompts, LLMs are guided through a structured workflow that mimics human expert behavior. This approach breaks down the complex task into multiple sub-steps, where LLMs complete simple tasks using internal knowledge or external tools. The final step integrates all findings to determine veracity. Compared to manual verification, FactAgent offers enhanced efficiency and provides transparent explanations at each step. Experimental studies demonstrate its effectiveness without requiring training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help stop fake news from spreading online. It uses special computers called large language models (LLMs) that can understand and process human language very well. These LLMs are usually used for tasks like chatbots or language translation, but this paper shows how they can be trained to detect fake news. The approach is designed to mimic how a human expert would verify the truth of a news claim, breaking it down into smaller steps that the LLMs can complete using their internal knowledge and external tools. This method is more efficient than having humans check every piece of news and provides explanations for why the LLMs think something is true or false. |
Keywords
» Artificial intelligence » Translation
