Summary of Instruction-tuned Language Models Are Better Knowledge Learners, by Zhengbao Jiang et al.
Instruction-tuned Language Models are Better Knowledge Learners
by Zhengbao Jiang, Zhiqing Sun, Weijia Shi, Pedro Rodriguez, Chunting Zhou, Graham Neubig, Xi Victoria Lin, Wen-tau Yih, Srinivasan Iyer
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed paper addresses a crucial challenge in large language model (LLM)-based assistants: adapting their factual knowledge through continued training on new data. The researchers identify the limitations of the standard approach, which involves pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs. They attribute this limitation to QA pairs being straightforward, whereas documents are more complex and intricate. To overcome this challenge, they propose a novel method called pre-instruction-tuning (PIT), where LLMs are first instruction-tuned on questions before continued pre-training on documents. Experimental results demonstrate that PIT significantly enhances the ability of LLMs to absorb knowledge from new documents, outperforming standard instruction-tuning by 17.8%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need to keep learning and updating their information so they can help us better. Right now, there’s a problem with how we update these models. They struggle to understand questions even though they’re good at understanding written text. The researchers think this is because questions are simpler than the complex documents they learn from. To fix this, they came up with a new way of training called pre-instruction-tuning. This method first teaches the model how to answer questions and then updates its knowledge based on new documents. In tests, this new approach worked much better than the old one. |
Keywords
* Artificial intelligence * Instruction tuning * Large language model
