| Challenge: | Existing methods for detecting fake news use only news embeddings to capture the lexical semantics between tokens. |
| Approach: | They propose a topic-based model with prompts to extract news embeddings from LLMs and a generalized page-rank model to extract local and global semantics. |
| Outcome: | The proposed model shows superior performance on five benchmark datasets over seven baseline methods. |
Similar Papers
Have LLMs Reopened the Pandora’s Box of AI-Generated Fake News? (2025.naacl-long)
Copied to clipboard
Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo, Bonam Mingole, S. Shyam Sundar, Sarah Rajtmajer, Amulya Yadav
| Challenge: | Large language models (LLMs) are increasingly being used by fake news creators to generate deceptive and persuasive content at scale. |
| Approach: | They propose to use large language models to generate fake news at scale and to assess the ability of human annotators and AI models to detect it. |
| Outcome: | The results show that LLMs are 68% more effective at detecting real news than humans, compared to humans and AI models for fake news detection. |
Adapting Fake News Detection to the Era of Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | a gap exists in understanding the interplay between machine-paraphrased real news, machine-generated fake news, and human-written real news . false information is easier to generate but harder to detect due to the bias of detectors against machine-generated texts . |
| Approach: | They propose a strategy to adapt fake news detectors to the era of large language models and AI-driven content creation . |
| Outcome: | The proposed detectors perform well on human-written articles but not vice versa . the proposed detector should be trained on datasets with lower machine-generated news ratio than the test set . |
A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
Copied to clipboard
Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
| Challenge: | Recent advances in large language models have led to an increase in synthetic content generation . the ability to detect LLMs-generated content has become of paramount importance . |
| Approach: | They propose to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and advocating for more adaptable and robust models to enhance detection accuracy. |
| Outcome: | The proposed model will be able to detect human-written content in real time. |
Detection of Human and Machine-Authored Fake News in Urdu (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fake news detection focus on binary classification and English texts, ignoring the distinction between machine-generated true vs. fake news and low-resource languages. |
| Approach: | They propose to include machine-generated news focusing on Urdu to improve accuracy and robustness. |
| Outcome: | The proposed strategy improves accuracy and robustness across four datasets in various settings. |
Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels, but their effective integration is non-trivial. |
| Approach: | They propose a global label propagation network with LLM-based pseudo labels for multimodal fake news detection which integrates LLM capabilities via label propagations. |
| Outcome: | The proposed model outperforms state-of-the-art models on benchmark datasets showing that it can propagate pseudo labels among all samples. |
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content (2025.findings-naacl)
Copied to clipboard
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain, Sahinur Rahman Laskar, Naeemul Hassan, Firoj Alam
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. |
| Approach: | They propose to develop a specialized LLM for analyzing news and social media content in a multilingual context. |
| Outcome: | The proposed model outperforms the current state-of-the-art on 23 testing sets and achieves comparable performance on 8 sets. |
A Survey on Natural Language Processing for Fake News Detection (2020.lrec-1)
Copied to clipboard
| Challenge: | Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety . |
| Approach: | They describe the challenges involved in fake news detection and describe related tasks . they outline promising research directions and highlight the difference between fake news and related tasks. |
| Outcome: | The proposed models are more fine-grained, detailed, fair, and practical. |
KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods of fake news detection focus on news entity information and ignore structured knowledge among news entities. |
| Approach: | They propose a model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs) they identify entities in news content and link them to entities in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and is competitive in the few-shot scenario. |
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)
Copied to clipboard
Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
| Challenge: | Large language models (LLMs) are capable of performing tasks but are likely to be misused. |
| Approach: | They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model . |
| Outcome: | The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts . |
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (D19-53)
Copied to clipboard
| Challenge: | Existing methods to distinguish between trusted and fake news articles lack feature engineering . et al. (2009) define fake news as the one which deliberately exposes real-world individuals, organisations and events to ridicule. |
| Approach: | They propose a graph neural network-based model which captures sentence interactions within a document. |
| Outcome: | The proposed model beats baselines and achieves state-of-the-art accuracy on existing datasets. |