Summary of Igot: Information Gain Optimized Tokenizer on Domain Adaptive Pretraining, by Dawei Feng et al.
IGOT: Information Gain Optimized Tokenizer on Domain Adaptive Pretraining
by Dawei Feng, Yihai Zhang, Zhixuan Xu
First submitted to arxiv on: 16 May 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 The paper proposes a novel approach to domain adaptation for large language models (LLMs) in natural language generation. Pretrained LLMs like ChatGPT have shown strong capabilities, but there are limitations when applying them to specific domains. The authors suggest that adding new knowledge to a pretrained model through continued training or fine-tuning is not sufficient and propose the Information Gain Optimized Tokenizer (IGOT). IGOT analyzes the special token set of downstream tasks, constructs a new subset using heuristic function , and builds a domain-specific tokenizer. This approach achieves significant improvements in pretraining efficiency, including 11.9% token saving, 12.2% training time saving, and 5.8% maximum GPU VRAM usage saving when combined with the LLaMA-7B model. The authors also demonstrate that this method can be effective in domain-specific tasks, reducing both the convergence radius and convergence point during pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models work better for specific tasks or areas of expertise. Right now, these models are great at doing things like writing emails or generating text, but they don’t always do well when applied to a specific field or topic. The authors propose a new way to adapt these models to specific domains by analyzing the special words and phrases used in that area. This approach helps the model learn more efficiently and use less resources, such as memory and processing power. The results show that this method can significantly improve the performance of large language models in domain-specific tasks. |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Llama » Pretraining » Token » Tokenizer
