Demystifying Instruction Mixing for Fine-tuning Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929247351734272 |
|---|---|
| author | Wang, Renxi Li, Haonan Wu, Minghao Wang, Yuxia Han, Xudong Zhang, Chiyu Baldwin, Timothy |
| author_facet | Wang, Renxi Li, Haonan Wu, Minghao Wang, Yuxia Han, Xudong Zhang, Chiyu Baldwin, Timothy |
| contents | Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10793 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Demystifying Instruction Mixing for Fine-tuning Large Language Models Wang, Renxi Li, Haonan Wu, Minghao Wang, Yuxia Han, Xudong Zhang, Chiyu Baldwin, Timothy Computation and Language Artificial Intelligence I.2.7 Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research. |
| title | Demystifying Instruction Mixing for Fine-tuning Large Language Models |
| topic | Computation and Language Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2312.10793 |