Disinformation Detection An Evolving Challenge In The Age Of Llms

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For more information see our F.A.Q. In a recent submission to the arXiv* server, researchers comprehensively examined the detection of large language models (LLMs)-generated misinformation. *Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial... The emergence of LLMs, including models such as Chat Generative Pre-Trained Transformer (ChatGPT) and Meta’s language model (Llama), has marked a significant milestone in computational social science (CSS). While LLMs have opened doors to extensive studies of human language and behavior, concerns about their potential misuse for disinformation have arisen. As these models advance in generating highly convincing human-like content, the risk of their exploitation for the creation of misleading information on a large scale becomes evident.

Recent research has highlighted this concern, acknowledging the cost-effectiveness and effectiveness of AI-generated disinformation. In text generation, there has been a transition from small language models (SLMs) to LLMs with billions of parameters, resulting in significant advancements. Models such as the Language Model for Dialogue Application (LaMDA), Bloom, Pathways Language Model (PaLM), and the generative pre-trained transformer (GPT) family have demonstrated the ability to produce human-level responses. However, the format of input prompts can influence performance, and advanced prompt engineering techniques are crucial to guide LLMs toward more accurate and higher-quality responses. Before the rise of LLMs, disinformation detection was primarily centered around SLMs such as bidirectional encoder representations from transformers (BERT), GPT-2, and text-to-text transfer transformers (T5). Deep learning has played a pivotal role in detecting disinformation, with models such as the hybrid deep model for fake news (CSI) and FakeBERT employing neural networks to identify textual features indicative of disinformation.

The introduction of LLMs, with their vast parameters, has significantly complicated disinformation detection, given their ability to produce natural, human-like text. This shift raises critical questions about the effectiveness of existing disinformation detection methods designed around SLMs.

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