Summary of Gpt Vs Retro: Exploring the Intersection Of Retrieval and Parameter-efficient Fine-tuning, by Aleksander Ficek et al.
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning
by Aleksander Ficek, Jiaqi Zeng, Oleksii Kuchaiev
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 This paper explores the application of Parameter-Efficient Fine-Tuning (PEFT) methods to large language models, focusing on adapting them while minimizing compute requirements. Specifically, it examines three PEFT techniques – P-tuning, Adapters, and LoRA – applied to a modified Retrieval-Enhanced Transformer (RETRO) model and a baseline GPT model across various sizes, ranging from 823 million to 48 billion parameters. The study reveals that RETRO models outperform GPT models in zero-shot settings due to their unique pre-training process, but GPT models have higher performance potential with PEFT. Additionally, the optimal balance between cost and performance is achieved using an 8B parameter model, with P-tuning lagging behind other PEFT techniques. The work also provides a comparative analysis of applying PEFT to an Instruction-tuned RETRO model versus a base RETRO model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at ways to make large language models more efficient while still being good at their job. It tests different methods for adapting these models and finds that some work better than others, depending on the specific task. The researchers also find that certain models are better at performing tasks without any extra training, while others need more training but can do the task really well. Overall, the goal is to make these models more useful and efficient for real-world applications. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Lora * Parameter efficient * Transformer * Zero shot
