Summary of Ideal: Leveraging Infinite and Dynamic Characterizations Of Large Language Models For Query-focused Summarization, by Jie Cao et al.
IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization
by Jie Cao, Dian Jiao, Qiang Yan, Wenqiao Zhang, Siliang Tang, Yueting Zhuang
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper investigates query-focused summarization (QFS), which generates summaries that answer specific questions. The authors leverage large language models’ (LLMs) capabilities to produce impressive textual understanding through pretraining, enabling extractive snippet generation. To harness the potential of LLMs-based QFS models, the study proposes two modules: Query-aware HyperExpert and Query-focused Infini-attention. These innovations facilitate broader applications in QFS technology. The paper presents extensive experiments on existing QFS benchmarks, demonstrating the effectiveness and generalizability of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making summaries that answer specific questions you have. This can help people get the information they need more easily. The authors use special language models to make this happen. They propose new ways to make these models work better. These ideas will help us create better summaries in the future. The authors tested their ideas on existing data and found that they worked well. |
Keywords
» Artificial intelligence » Attention » Pretraining » Summarization
