Summary of 60 Data Points Are Sufficient to Fine-tune Llms For Question-answering, by Junjie Ye et al.
60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
by Junjie Ye, Yuming Yang, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan
First submitted to arxiv on: 24 Sep 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 Large language models (LLMs) are trained on vast datasets, allowing them to absorb extensive world knowledge. However, strategies for fine-tuning LLMs for question-answering tasks have received limited attention. This paper categorizes supervised fine-tuning (SFT) data based on the extent of knowledge memorized by pre-trained LLMs and explores three key factors: SFT dataset requirements, impact on model performance, and varying data needs across different LLM models. The results show that minimal SFT data can activate pre-trained knowledge, enabling LLMs to perform question-answering tasks. Furthermore, SFT with diverse memory levels has a significant impact on LLM performance, with optimal datasets depending on the specific model being fine-tuned. This research lays groundwork for deeper understanding of these phenomena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving computer models that can answer questions. These models are trained on huge amounts of information and then get better at answering questions when they’re taught more things. The researchers in this study figured out how to make the models learn faster and do a better job by giving them smaller amounts of new information. They also discovered that different models work best with different types of information. This research helps us understand how these models can be improved, which is important for things like searching the internet or helping people answer questions. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Question answering » Supervised
