Authors: Eun Cheol Choi, Emilio Ferrara

Published on: February 08, 2024

Impact Score: 8.05

Arxiv code: Arxiv:2402.05904

Summary

• What is new: FACT-GPT introduces an automated system for the claim matching stage of fact-checking, leveraging Large Language Models (LLMs) trained on a synthetic dataset.
• Why this is important: The spread of misinformation is damaging public health and trust, necessitating an efficient fact-checking process.
• What the research proposes: By automating the claim matching stage with FACT-GPT, the process becomes faster and can handle larger volumes of information, assisting human fact-checkers.
• Results: The specialized LLMs within FACT-GPT can match the accuracy of larger models in identifying related claims, effectively mirroring human judgment.

Technical Details

Technological frameworks used: FACT-GPT utilizes Large Language Models.

Models used: Specialized Large Language Models trained on a synthetic dataset.

Data used: Synthetic dataset tailored for claim matching.

Potential Impact

This research could impact the social media, news agencies, and public health organizations by improving the accuracy and efficiency of fact-checking processes.

Want to implement this idea in a business?

We have generated a startup concept here: TruthGuard.