Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights. |
| Approach: | They compare unsupervised fine-tuning and retrieval-augmented generation approaches to learning new factual information. |
| Outcome: | The proposed models outperform unsupervised fine-tuning and retrieval-augmented generation (RAG) on knowledge-intensive tasks across different topics. |
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