Conference item
Improving language model predictions via prompts enriched with knowledge graphs
- Abstract:
- Despite advances in deep learning and knowledge graphs (KGs), using language models for natural language understanding and question answering remains a challenging task. Pre-trained language models (PLMs) have shown to be able to leverage contextual information, to complete cloze prompts, next sentence completion and question answering tasks in various domains. Unlike structured data querying in e.g. KGs, mapping an input question to data that may or may not be stored by the language model is not a simple task. Recent studies have highlighted the improvements that can be made to the quality of information retrieved from PLMs by adding auxiliary data to otherwise naive prompts. In this paper, we explore the effects of enriching prompts with additional contextual information leveraged from the Wikidata KG on language model performance. Specifically, we compare the performance of naive vs. KG-engineered cloze prompts for entity genre classification in the movie domain. Selecting a broad range of commonly available Wikidata properties, we show that enrichment of cloze-style prompts with Wikidata information can result in a significantly higher recall for the investigated BERT and RoBERTa large PLMs. However, it is also apparent that the optimum level of data enrichment differs between models.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.2MB, Terms of use)
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- Publication website:
- https://ceur-ws.org/Vol-3342/
Authors
- Publisher:
- CEUR Workshop Proceedings
- Host title:
- Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2022)
- Article number:
- 3
- Series:
- CEUR Workshop Proceedings
- Series number:
- 3342
- Publication date:
- 2023-02-11
- Acceptance date:
- 2022-09-29
- Event title:
- Workshop on Deep Learning for Knowledge Graphs (DL4KG@ISWC)
- Event location:
- Virtual event
- Event website:
- https://alammehwish.github.io/dl4kg2022/
- Event start date:
- 2022-10-24
- Event end date:
- 2022-10-24
- ISSN:
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1613-0073
- Language:
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English
- Keywords:
- Pubs id:
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1328783
- Local pid:
-
pubs:1328783
- Deposit date:
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2023-02-17
Terms of use
- Copyright holder:
- Brate et al.
- Copyright date:
- 2022
- Rights statement:
- © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
- Licence:
- CC Attribution (CC BY)
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