Summary of Quest: Query-centric Data Synthesis Approach For Long-context Scaling Of Large Language Model, by Chaochen Gao et al.
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model
by Chaochen Gao, Xing Wu, Qi Fu, Songlin Hu
First submitted to arxiv on: 30 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 introduces a new method called Quest for extending the contexts of large language models (LLMs) to handle complex tasks. The traditional approaches use filtered long documents, but they lead to domain imbalances, limiting model performance. Quest uses a generative model to predict potential queries for each document and groups documents with similar queries and keywords. This approach balances semantic coherence and diversity, outperforming existing methods on long-context tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Quest is a new method that helps large language models (LLMs) handle complex tasks by extending their contexts. Right now, LLMs can only understand short texts, but Quest lets them learn from longer texts too! It’s like a special way to mix and match words from many documents to make the model smarter. This makes the model better at answering questions that require understanding lots of information. |
Keywords
» Artificial intelligence » Generative model
