Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022.emnlp-main)
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| Challenge: | Recent research shows that relevant knowledge can provide useful context for commonsense tasks. |
| Approach: | They propose a method that learns to generate contextually relevant knowledge in response to given questions. |
| Outcome: | The proposed method shows consistent gains over 9 commonsense benchmarks. |
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| Challenge: | elaborations are generated using language models that generate background knowledge that helps improve performance . human evaluations show that the quality of the generated ellaborations is high . |
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| Challenge: | Existing knowledge-augmented reasoning methods fail to capture the *introspective* nature of knowledge required in commonsense reasoning. |
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| Challenge: | Large Language Models (LLMs) can improve commonsense reasoning by generating intermediate knowledge, but the effectiveness of this knowledge introspection is not always guaranteed. |
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| Challenge: | Current Large Language Models (LLMs) have shown strong reasoning capabilities in commonsense question answering benchmarks, but the process underlying their success remains largely opaque. |
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| Challenge: | Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts. |
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| Challenge: | Existing knowledge-aware QA models do not have commonsense and background knowledge to answer nontrivial questions. |
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| Challenge: | Question answering models have access to two sources of knowledge during inference time: parametric knowledge and contextual knowledge. |
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| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
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