Summary of Catching Chameleons: Detecting Evolving Disinformation Generated Using Large Language Models, by Bohan Jiang et al.
Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models
by Bohan Jiang, Chengshuai Zhao, Zhen Tan, Huan Liu
First submitted to arxiv on: 26 Jun 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 This paper proposes DELD (Detecting Evolving LLM-generated Disinformation), a novel approach to detecting evolving large language model (LLM)-generated disinformation. The problem is challenging due to the constant evolution of disinformation through rapid developments in LLMs and their variants. Existing methods struggle with training separate models for each generator, and performance decreases when encountering sequential orders of evolving disinformation. DELD jointly leverages pre-trained language models’ fact-checking capabilities and independent disinformation generation characteristics from various LLMs. It uses concatenated semantic embeddings and trainable soft prompts to elicit model-specific knowledge, addressing label scarcity. The proposed method outperforms state-of-the-art methods and provides valuable insights into unique disinformation patterns across different LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to detect fake news created by special computer programs called large language models (LLMs). Right now, it’s hard to keep up with the constant changes in these fake news stories because new LLMs are being developed all the time. The existing methods for detecting fake news aren’t very good at dealing with these changes. The researchers propose a new way to detect evolving fake news by combining the strengths of different computer models and special prompts that help them learn what makes each type of fake news unique. Their method works really well and gives us valuable insights into how these fake news stories are created. |
Keywords
» Artificial intelligence » Large language model
