Automatic Fact-checking in English and Telugu
This work addresses the challenge of manual fact-checking for English and Telugu speakers, but it is incremental as it focuses on benchmarking existing methods on new data.
The paper tackled the problem of false information by experimenting with large language models to classify factual claims and generate justifications in English and Telugu, resulting in the creation of a bilingual dataset and benchmarking of different veracity classification approaches.
False information poses a significant global challenge, and manually verifying claims is a time-consuming and resource-intensive process. In this research paper, we experiment with different approaches to investigate the effectiveness of large language models (LLMs) in classifying factual claims by their veracity and generating justifications in English and Telugu. The key contributions of this work include the creation of a bilingual English-Telugu dataset and the benchmarking of different veracity classification approaches based on LLMs.