Papers › Prompting as Probing: Using Language Models for Knowledge Base Construction

Prompting as Probing: Using Language Models for Knowledge Base Construction

23 Aug 2022arXiv:2208.11057archive 2025-07-28

Dimitrios Alivanistos, Selene Báez Santamaría, Michael Cochez, Jan-Christoph Kalo, Emile van Krieken, Thiviyan Thanapalasingam

Language Models (LMs) have proven to be useful in various downstream applications, such as summarisation, translation, question answering and text classification. LMs are becoming increasingly important tools in Artificial Intelligence, because of the vast quantity of information they can store. In this work, we present ProP (Prompting as Probing), which utilizes GPT-3, a large Language Model originally proposed by OpenAI in 2020, to perform the task of Knowledge Base Construction (KBC). ProP implements a multi-step approach that combines a variety of prompting techniques to achieve this. Our results show that manual prompt curation is essential, that the LM must be encouraged to give answer sets of variable lengths, in particular including empty answer sets, that true/false questions are a useful device to increase precision on suggestions generated by the LM, that the size of the LM is a crucial factor, and that a dictionary of entity aliases improves the LM score. Our evaluation study indicates that these proposed techniques can substantially enhance the quality of the final predictions: ProP won track 2 of the LM-KBC competition, outperforming the baseline by 36.4 percentage points. Our implementation is available on https://github.com/HEmile/iswc-challenge.

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clean_object hemile/iswc-challenge/evaluate.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 5daf709fbac40821 · report
create_prompt hemile/iswc-challenge/baseline.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 72ee5ba094d0c0bb · report
create_prompt hemile/iswc-challenge/gpt3_baseline.py official repository ran · our draft was wrong MIT (permissive) · 6fd929182163484e · report
is_none_gts hemile/iswc-challenge/evaluate.py official repository ran · violated contract fingerprinted MIT (permissive) · 36ab981dfffbc447 · report
is_none_preds hemile/iswc-challenge/evaluate.py official repository ran · violated contract MIT (permissive) · 370e1ea65710838a · report
load_prompt hemile/iswc-challenge/gpt3_baseline.py official repository ran · our draft was wrong MIT (permissive) · bc5c5e8713300ce5 · report

Tasks

Knowledge Base ConstructionLanguage ModelingLanguage ModellingLarge Language ModelQuestion AnsweringText Classification

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Methods

AdamAttentionAttention DropoutBASEBPECosine AnnealingDense ConnectionsDropoutGPT-3Layer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

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