Kavli Affiliate: Long Zhang
| First 5 Authors: Hongyao Tu, Hongyao Tu, , ,
| Summary:
The goal of open relation extraction (OpenRE) is to develop an RE model that
can generalize to new relations not encountered during training. Existing
studies primarily formulate OpenRE as a clustering task. They first cluster all
test instances based on the similarity between the instances, and then manually
assign a new relation to each cluster. However, their reliance on human
annotation limits their practicality. In this paper, we propose an OpenRE
framework based on large language models (LLMs), which directly predicts new
relations for test instances by leveraging their strong language understanding
and generation abilities, without human intervention. Specifically, our
framework consists of two core components: (1) a relation discoverer (RD),
designed to predict new relations for test instances based on
textitdemonstrations formed by training instances with known relations; and
(2) a relation predictor (RP), used to select the most likely relation for a
test instance from $n$ candidate relations, guided by textitdemonstrations
composed of their instances. To enhance the ability of our framework to predict
new relations, we design a self-correcting inference strategy composed of three
stages: relation discovery, relation denoising, and relation prediction. In the
first stage, we use RD to preliminarily predict new relations for all test
instances. Next, we apply RP to select some high-reliability test instances for
each new relation from the prediction results of RD through a cross-validation
method. During the third stage, we employ RP to re-predict the relations of all
test instances based on the demonstrations constructed from these reliable test
instances. Extensive experiments on three OpenRE datasets demonstrate the
effectiveness of our framework. We release our code at
https://github.com/XMUDeepLIT/LLM-OREF.git.
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