Summary of Time Sensitive Knowledge Editing Through Efficient Finetuning, by Xiou Ge et al.
Time Sensitive Knowledge Editing through Efficient Finetuning
by Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
First submitted to arxiv on: 6 Jun 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 paper proposes an innovative approach to updating and expanding Large Language Models (LLMs) with Parameter-Efficient Fine-Tuning (PEFT) techniques. The current methods, such as locate-and-edit, suffer from limitations like poor performance on complex queries and long processing times, making them impractical for large-scale knowledge editing. To overcome these challenges, the authors curate a comprehensive temporal dataset for benchmarking KE performance and investigate the impact of fine-tuning on various layers in an LLM for multi-hop question answering tasks. The results show that PEFT outperforms locate-and-edit techniques for time-sensitive knowledge updates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human-like text. But, it’s hard to keep them up-to-date once they’re trained. This paper finds a new way to update these models using something called Parameter-Efficient Fine-Tuning (PEFT). The current method, locate-and-edit, has some big problems – it doesn’t do well on tricky questions and takes too long to work. To fix this, the authors created a special dataset to test how good PEFT is at updating language models. They also looked at how fine-tuning different parts of the model affects its ability to answer hard questions. The results show that PEFT is better than locate-and-edit for making sure these language models are accurate and up-to-date. |
Keywords
* Artificial intelligence * Fine tuning * Parameter efficient * Question answering
