Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News

• URL: http://arxiv.org/abs/2406.14012v1
• Date: Thu, 20 Jun 2024 06:02:04 GMT
• Title: Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
• Authors: Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee,
• Abstract summary: This paper aims to elucidate simple markers that help individuals distinguish between articles penned by humans and those created by LLMs. We then devise a metric named Entropy-Shift Authorship Signature (ESAS) based on the information theory and entropy principles. The proposed ESAS ranks terms or entities, like POS tagging, within news articles based on their relevance in discerning article authorship.
• Score: 0.38233569758620056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLMs offer valuable capabilities, yet they can be utilized by malicious users to disseminate deceptive information and generate fake news. The growing prevalence of LLMs poses difficulties in crafting detection approaches that remain effective across various text domains. Additionally, the absence of precautionary measures for AI-generated news on online social platforms is concerning. Therefore, there is an urgent need to improve people's ability to differentiate between news articles written by humans and those produced by LLMs. By providing cues in human-written and LLM-generated news, we can help individuals increase their skepticism towards fake LLM-generated news. This paper aims to elucidate simple markers that help individuals distinguish between articles penned by humans and those created by LLMs. To achieve this, we initially collected a dataset comprising 39k news articles authored by humans or generated by four distinct LLMs with varying degrees of fake. We then devise a metric named Entropy-Shift Authorship Signature (ESAS) based on the information theory and entropy principles. The proposed ESAS ranks terms or entities, like POS tagging, within news articles based on their relevance in discerning article authorship. We demonstrate the effectiveness of our metric by showing the high accuracy attained by a basic method, i.e., TF-IDF combined with logistic regression classifier, using a small set of terms with the highest ESAS score. Consequently, we introduce and scrutinize these top ESAS-ranked terms to aid individuals in strengthening their skepticism towards LLM-generated fake news.

Related papers

• The Reopening of Pandora's Box: Analyzing the Role of LLMs in the Evolving Battle Against AI-Generated Fake News [30.759761034237563]
  Large language models (LLMs) produce convincing fake news at scale. LLMs are 68% more effective at detecting real news than humans. For fake news detection, the performance of LLMs and humans remains comparable.
  arXiv  Detail & Related papers  (2024-10-25T01:58:29Z)
• From Deception to Detection: The Dual Roles of Large Language Models in Fake News [0.20482269513546458]
  Fake news poses a significant threat to the integrity of information ecosystems and public trust. The advent of Large Language Models (LLMs) holds considerable promise for transforming the battle against fake news. This paper explores the capability of various LLMs in effectively combating fake news.
  arXiv  Detail & Related papers  (2024-09-25T22:57:29Z)
• LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection [34.984605500444324]
  Large Language Models (LLMs) are known for their powerful natural language understanding and explanation generation abilities. We propose LLM-GAN, a novel framework that utilizes prompting mechanisms to enable an LLM to become Generator and Detector. Our results demonstrate LLM-GAN's effectiveness in both prediction performance and explanation quality.
  arXiv  Detail & Related papers  (2024-09-03T11:06:45Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation [58.31430028519306]
  This study explores how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process. While LLMs alone can generate satisfactory news headlines, on average, human control is needed to fix undesirable model outputs.
  arXiv  Detail & Related papers  (2023-10-16T15:11:01Z)
• Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
  We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
  arXiv  Detail & Related papers  (2023-10-10T03:06:38Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection [22.658378054986624]
  Large language models (LLMs) have shown remarkable performance in various tasks. LLMs provide desirable multi-perspective rationales but still underperform the basic SLM, fine-tuned BERT. We propose that current LLMs may not substitute fine-tuned SLMs in fake news detection but can be a good advisor for SLMs.
  arXiv  Detail & Related papers  (2023-09-21T16:47:30Z)
• Fake News Detectors are Biased against Texts Generated by Large Language Models [39.36284616311687]
  The spread of fake news has emerged as a critical challenge, undermining trust and posing threats to society. We present a novel paradigm to evaluate fake news detectors in scenarios involving both human-written and LLM-generated misinformation.
  arXiv  Detail & Related papers  (2023-09-15T18:04:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.