Summary of Knowla: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation, by Xindi Luo and Zequn Sun and Jing Zhao and Zhe Zhao and Wei Hu
KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
by Xindi Luo, Zequn Sun, Jing Zhao, Zhe Zhao, Wei Hu
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 In this paper, researchers investigate ways to improve the performance of large language models (LLMs) on specific tasks by leveraging knowledge graph embeddings. They propose a novel adaptation method called KnowLA, which inserts an adaptation layer into the LLM to incorporate entity embeddings from the input text. The adaptation layer is trained jointly with LoRA on instruction data. Experiments demonstrate the effectiveness and robustness of KnowLA across six benchmarks using two popular LLMs and three knowledge graphs. The results show that this approach can activate relevant parameterized knowledge in the LLM without modifying its parameters or input prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being adapted to specific tasks by fine-tuning their parameters. Researchers have found a way to make this process more efficient using knowledge graph embeddings. They created an adaptation layer that helps the model understand what it’s learning from, making it better at answering questions without changing its underlying structure. |
Keywords
* Artificial intelligence * Fine tuning * Knowledge graph * Lora
