Summary of Esqa: Event Sequences Question Answering, by Irina Abdullaeva et al.
ESQA: Event Sequences Question Answering
by Irina Abdullaeva, Andrei Filatov, Mikhail Orlov, Ivan Karpukhin, Viacheslav Vasilev, Denis Dimitrov, Andrey Kuznetsov, Ivan Kireev, Andrey Savchenko
First submitted to arxiv on: 3 Jul 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 The paper proposes a novel approach to modeling event sequences (ESs) using large language models (LLMs). ESs are ubiquitous in various domains, including finance, retail, social networks, and healthcare. The proposed method, called ESQA, addresses common challenges of processing long sequences and improving time and numeric features processing. By leveraging the power of LLMs, ESQA achieves state-of-the-art results in the ESs domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to use large language models for event sequence modeling. Event sequences are important in many areas like finance and healthcare. The method, called ESQA, is good at handling long sequences and improving time and numeric features processing. This helps achieve better results than before. |
