Summary of Longalign: a Recipe For Long Context Alignment Of Large Language Models, by Yushi Bai et al.
LongAlign: A Recipe for Long Context Alignment of Large Language Models
by Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, Juanzi Li
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 research presents a novel approach to fine-tuning large language models (LLMs) for effective handling of long contexts. The authors introduce LongAlign, a recipe consisting of instruction data construction, training, and evaluation strategies for long context alignment. To build the instruction-following dataset, they utilize Self-Instruct, covering various tasks from diverse sources. They also develop packing and sorted batching strategies to accelerate supervised fine-tuning on datasets with varying length distributions. Additionally, they propose a loss weighting method to balance sequence contributions during packing training. LongAlign is evaluated on the newly introduced LongBench-Chat benchmark, which assesses instruction-following capabilities on queries of 10k-100k in length. The results show that LongAlign outperforms existing recipes for LLMs by up to 30% in long context tasks while maintaining proficiency in handling short, generic tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better language models that can understand really long pieces of text! The researchers created a new way to train these models, called LongAlign. They made a special dataset and some tricks for training the model to work well with long texts. Then, they tested their method on a bunch of different tasks and showed it works really well – even better than other ways people have tried. This is important because we use language models all the time, and being able to understand longer texts can help us do things like have more natural conversations with computers. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Supervised
