Summary of Ta’keed: the First Generative Fact-checking System For Arabic Claims, by Saud Althabiti et al.
Ta’keed: The First Generative Fact-Checking System for Arabic Claims
by Saud Althabiti, Mohammad Ammar Alsalka, Eric Atwell
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Ta’keed is an innovative Arabic automatic fact-checking system that goes beyond simply classifying claims as true or false by generating explanations for credibility assessments. This paper explores the limited research in generating explanations, particularly in Arabic, and addresses this gap with two main components: information retrieval and LLM-based claim verification. The proposed model achieved a promising F1 score of 0.72 in classification tasks using the ArFactEx dataset. Furthermore, the system’s generated explanations were compared to gold-standard explanations syntactically and semantically, revealing an average cosine similarity score of 0.76. Additionally, the study investigated the impact of varying snippet quantities on claim classification accuracy, finding a potential correlation with the top seven hits outperforming others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ta’keed is a new system that helps check if statements are true or not, and also explains why they are true or false. Right now, most systems just say yes or no without explaining themselves. This paper tries to fix this by making a better system for Arabic language. The system has two parts: one finds information and the other checks the information. They tested it on some data and got good results. It’s like a super smart helper that can explain things! |
Keywords
» Artificial intelligence » Classification » Cosine similarity » F1 score
