Collaboration of Large and Small Models for Event Type Discovery in Completely Open Domains

Jiaxu Li, Bin Ge (), Hao Xu, Peixin Huang and Lihua Liu
Additional contact information
Jiaxu Li: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Bin Ge: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Hao Xu: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Peixin Huang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Lihua Liu: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 18, 1-15

Abstract: Event type discovery in open domains aims to automate the induction of event types from completely unlabeled text data. Conventionally, small models utilize clustering techniques to address this task. Nonetheless, the fully unsupervised nature of these methods results in suboptimal performance by small models in this context. Recently, large language models (LLMs) have demonstrated excellent capabilities in contextual understanding, providing additional relevant information for specific task scenarios, albeit with challenges in precision and cost effectiveness. In this paper, we use LLM to guide the clustering of event texts and distill this process into a fine-tuning task for training smaller pre-trained language models. This approach enables effective event type discovery even in scenarios lacking annotated data. The study unfolds in three stages: in action acquisition, leveraging LLMs to extract type-relevant information from each event text, ensuring that the event representations are particular to task-specific details; in clustering refinement and dual-fine-tune, LLMs refine results from both task-agnostic and task-specific perspectives, with the refinement process designed as fine-tuning tasks under different viewpoints to adjust encoders; and in type generation, post-clustering, LLMs generate meaningful event type labels for each cluster. Experiments show that our method outperforms current state-of-the-art approaches and excels in event type discovery tasks even in completely open-domain with no labeled data.

Keywords: event type discovery; open domains; large language model; text clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/18/2928/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/18/2928/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:18:p:2928-:d:1482112

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().