The proliferation of misinformation and fake news on social media platforms poses a significant challenge in today’s digital age. Traditional automated fake news detection systems often struggle with the complexity of the task, lacking the ability to provide detailed explanations and interpret nuanced contextual information. This thesis explores the enhancement of fake news detection using Multi-Agent LLM frameworks. By emulating human expert behavior, the proposed system employs specialized LLM agents, each focusing on a distinct aspect of the problem. This research investigates the effectiveness of using Multi-Agent LLM frameworks to enhance fake news detection, in comparison to traditional machine learning models and single-agent LLM systems. It also examines the impact of integrating real-time web knowledge and the optimal design and composition of such frameworks. This innovative approach aims to provide comprehensive, human-like explanations for its assessments, enhancing user trust and interpretability, while maintaining the speed and scalability of automated solutions.
Thesis Type |
|
Student |
Daniel Valchanov |
Status |
Running |
Presentation room |
Seminar room I5 6202 |
Supervisor(s) |
stefan@stefandecker.org |
Advisor(s) |
Fateme Fathi |
Contact |
fathi@dbis.rwth-aachen.de |
Prerequisites:
- Machine Learning and Natural Language Processing (NLP) fundamentals
- Text processing techniques (e.g., tokenization, stemming, lemmatization).
- Knowledge of word embeddings and vector space models (e.g., Word2Vec, GloVe).
- Large Language Models (LLMs)
- Familiarity with LLMs (e.g., GPTs, Gemini, Cluade, and Deepseek).
- Experience with using LLM APIs (e.g., OpenAI, Gemini, DeepSeek).
- Familiarity with prompt engineering and fine-tuning techniques.
- Basic understanding of Multi-Agent Systems and frameworks (e.g. AutoGen)
- Programming Languages – HTML, Javascript, CSS, and Python.
- Familiarity with UX design principles to create an intuitive user interface.