Defending Against Disinformation Attacks in Open-Domain Question Answering (2024.eacl-short)
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| Challenge: | Existing methods to defend against data poisoning attacks in open-domain question answering are lacking . |
| Approach: | They propose a method that uses query augmentation to find diverse passages that could answer the original question but are less likely to have been poisoned. |
| Outcome: | The proposed method provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts. |
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| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
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Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts (2024.findings-emnlp)
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Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
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