Extracting Multi-valued Relations from Language Models
Sneha Singhania, Simon Razniewski, Gerhard Weikum · 2023
The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge.However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct.To overcome this limitation, we analyze these representations for their potential to yield materialized multi-object relational knowledge.We formulate the problem as a rank-then-select task.For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge.Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score.Our results highlight the difficulty of employing LMs for the multi-valued slot-filling task, and pave the way for further research on extracting relational knowledge from latent language representations.