ESQA: Event Sequences Question Answering
This work addresses the challenge of event sequence analysis for applications in finance, retail, social networks, and healthcare, representing an incremental advancement in adapting LLMs to this domain.
The paper tackled the problem of adapting large language models to event sequences, which are common in domains like finance and healthcare, by proposing ESQA, a method that handles long sequences and improves time and numeric feature processing, achieving state-of-the-art results in the event sequences domain.
Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as a special type of tabular data with annotated timestamps. Despite the importance of ESs modeling and analysis, little effort was made in adapting large language models (LLMs) to the ESs domain. In this paper, we highlight the common difficulties of ESs processing and propose a novel solution capable of solving multiple downstream tasks with little or no finetuning. In particular, we solve the problem of working with long sequences and improve time and numeric features processing. The resulting method, called ESQA, effectively utilizes the power of LLMs and, according to extensive experiments, achieves state-of-the-art results in the ESs domain.