Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation (2024.naacl-long)
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| Challenge: | Existing methods to generate counter-misinformation responses are often trained end-to-end without external knowledge, resulting in subpar text quality and excessively repetitive responses. |
| Approach: | They propose retrieval augmented response generation for online misinformation (RARG) that collects supporting evidence and generates counter-misinformation responses via reinforcement learning from human feedback. |
| Outcome: | The proposed method outperforms baselines with extensive experiments with in- and cross-domain datasets and consistently generates high-quality counter-misinformation responses. |
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