Summary of Evidence-driven Retrieval Augmented Response Generation For Online Misinformation, by Zhenrui Yue et al.
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
by Zhenrui Yue, Huimin Zeng, Yimeng Lu, Lanyu Shang, Yang Zhang, Dong Wang
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A new approach to combating online misinformation is proposed, leveraging text generation techniques to produce polite and fact-based counter-misinformation responses. The existing methods are limited as they are trained end-to-end without incorporating external knowledge, resulting in low-quality texts and repetitive responses. The authors introduce retrieval augmented response generation for online misinformation (RARG), which collects evidence from scientific sources and generates responses based on that evidence. RARG consists of two stages: evidence collection using a database of over 1M academic articles, and response generation via reinforcement learning from human feedback. A reward function is designed to maximize the utilization of retrieved evidence while maintaining text quality, yielding polite and factual responses that effectively refute misinformation. The approach is demonstrated on COVID-19 data sets, outperforming baselines with high-quality counter-misinformation responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to stop online lies is being developed. Right now, people try to fight fake news, but they often use rude language and don’t provide proof. To fix this, scientists are using computer programs to create responses that are polite, accurate, and based on evidence from trustworthy sources. These programs can collect information from millions of academic articles and then use that information to write a response that is clear and easy to understand. The goal is to make sure the response is not only correct but also nice and respectful. This new method was tested using COVID-19 data and it worked better than other methods at creating responses that were helpful in stopping misinformation. |
Keywords
* Artificial intelligence * Reinforcement learning from human feedback * Text generation
