Summary of Neutral Residues: Revisiting Adapters For Model Extension, by Franck Signe Talla and Herve Jegou and Edouard Grave
Neutral residues: revisiting adapters for model extension
by Franck Signe Talla, Herve Jegou, Edouard Grave
First submitted to arxiv on: 3 Oct 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 A novel approach to domain adaptation is proposed, focusing on extending a pre-trained large language model to a new domain without being seen during training. The existing methods like fine-tuning or low-rank adaptation are successful but do not add any extra capacity and degrade performance in the original domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers aim to improve the performance of a large language model when applied to a new domain that was not present during training. They explore ways to adapt the model without sacrificing its original capabilities, highlighting the limitations of current methods like fine-tuning or low-rank adaptation. |
Keywords
* Artificial intelligence * Domain adaptation * Fine tuning * Large language model * Low rank adaptation
