Summary of Star: Constraint Lora with Dynamic Active Learning For Data-efficient Fine-tuning Of Large Language Models, by Linhai Zhang et al.
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
by Linhai Zhang, Jialong Wu, Deyu Zhou, Guoqiang Xu
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 for Large Language Models (LLMs) is presented in this paper, aiming to address the issue of large annotated data consumption through Data-Efficient Fine-Tuning. By combining Parameter-Efficient Fine-Tuning (PEFT) with active learning and incorporating uncertainty-based methods, the proposed approach outperforms existing baseline models on three complex reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have shown impressive capabilities in few-shot learning, but supervised training is still necessary for complex tasks. To address this challenge, researchers have developed various fine-tuning methods to reduce data consumption. This paper takes a different approach by combining PEFT with active learning and introducing novel uncertainty-based methods. The proposed approach shows improved results on three complex reasoning tasks. |
Keywords
» Artificial intelligence » Active learning » Few shot » Fine tuning » Parameter efficient » Supervised
