Summary of Learning to Generate and Evaluate Fact-checking Explanations with Transformers, by Darius Feher et al.
Learning to Generate and Evaluate Fact-checking Explanations with Transformers
by Darius Feher, Abdullah Khered, Hao Zhang, Riza Batista-Navarro, Viktor Schlegel
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper develops transformer-based fact-checking models that generate explanations for their decisions, contributing to the field of Explainable Artificial Intelligence (XAI). The models evaluate explanations across dimensions such as self-contradiction, hallucination, convincingness, and overall quality. By introducing human-centered evaluation methods and developing specialized datasets, the approach emphasizes the importance of aligning AI-generated explanations with human judgments. This research advances theoretical knowledge in XAI and has practical implications for enhancing transparency, reliability, and user trust in AI-driven fact-checking systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates new AI models that can check if information is true or not and explain why they think that way. They also create a special way to measure how good these explanations are. The goal is to make sure AI machines are giving us reliable answers, which we can understand and trust. This research helps make AI better at explaining itself and can be used in many applications like fact-checking news articles. |
Keywords
» Artificial intelligence » Hallucination » Transformer
