Summary of Towards Better Generalization in Open-domain Question Answering by Mitigating Context Memorization, By Zixuan Zhang et al.
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization
by Zixuan Zhang, Revanth Gangi Reddy, Kevin Small, Tong Zhang, Heng Ji
First submitted to arxiv on: 2 Apr 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 This research paper investigates the challenges of open-domain question answering (OpenQA) models in adapting to updated knowledge and transferring to new domains. The authors propose a retrieval-augmented QA model that struggles with over-reliance on memorizing external knowledge, hindering its ability to generalize. To address this issue, they introduce Corpus-Invariant Tuning (CIT), a training strategy that controls the likelihood of retrieved contexts during training. The results show significant improvements in generalizability without compromising performance in original domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Open-domain question answering tries to answer factual questions using a large knowledge base. But what happens when new information comes out? This paper looks at how well these models can adapt to changing knowledge and switch to new topics. They find that the problem is that the models memorize too much old information, making it hard for them to learn new things. To fix this, they suggest a simple way to train the model called Corpus-Invariant Tuning (CIT). It makes the model better at learning new things without forgetting what it already knows. |
Keywords
* Artificial intelligence * Knowledge base * Likelihood * Question answering
