Self-Knowledge Guided Retrieval Augmentation for Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown superior performance without task-specific fine-tuning due to the computational costs. |
| Approach: | They propose a method which lets LLMs refer to the questions they have previously encountered and adaptively call for external resources when dealing with new questions. |
| Outcome: | The proposed method outperforms chain-of-thought based and fully retrieval-based methods on multiple datasets and outperformed chain- of-though, chatGPT and InstructGPT. |
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Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. |
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