Summary of Acer: Automatic Language Model Context Extension Via Retrieval, by Luyu Gao et al.
ACER: Automatic Language Model Context Extension via Retrieval
by Luyu Gao, Yunyi Zhang, Jamie Callan
First submitted to arxiv on: 11 Oct 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 A novel approach to developing task-specific long-context modeling capabilities is proposed in this paper. Currently, open-weight generalist long-context models struggle with practical processing tasks, requiring costly task-specific data. The authors draw inspiration from human information processing, where a retrieval stage ranks documents and the reader focuses on top candidates. An automatic data synthesis pipeline mimics this process using short-context language models (LMs). These LMs are then fine-tuned using self-generated data to obtain task-specific long-context capabilities. This paper demonstrates that the short-context model can bootstrap over synthetic data, outperforming generalist long-context models and the retrieval-read pipeline used for training data in real-world tasks like long-context retrieval augmented generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are working on a way to make computers better at understanding long pieces of information. Right now, computer language models aren’t very good at doing this, so they need special training data. The idea is to mimic how humans process lots of information by first finding the most important parts and then focusing on those. This paper shows how to create a special kind of training data that helps computers get better at understanding long pieces of text. |
Keywords
» Artificial intelligence » Retrieval augmented generation » Synthetic data
