Extracting Multi-valued Relations from Language Models

• URL: http://arxiv.org/abs/2307.03122v2
• Date: Fri, 7 Jul 2023 07:25:36 GMT
• Title: Extracting Multi-valued Relations from Language Models
• Authors: Sneha Singhania, Simon Razniewski, Gerhard Weikum
• Abstract summary: We analyze latent language representations for their potential to yield materialized multi-object relational knowledge. 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.
• Score: 36.944060044138304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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