mlsanityData to 14 Oct 2026

SPE: Symmetrical Prompt Enhancement for Fact Probing

arXiv 2211.07078 ↗First posted 14 Nov 2022cs.CL cs.AI cs.LGcited by 5

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Abstract (arXiv)

Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs for the extent of this knowledge through prompts either in discrete or continuous forms. However, these methods do not consider symmetry of the task: object prediction and subject prediction. In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction. Our results on a popular factual probing dataset, LAMA, show significant improvement of SPE over previous probing methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

21 of 33 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  4. 2020Extracting Training Data from Large Language Modelscited by 2,189
  5. 2019Language Models as Knowledge Bases?cited by 1,936
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  8. 2021GPT Understands, Toocited by 716
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  10. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 445
  11. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  12. 2021Factual Probing Is [MASK]: Learning vs. Learning to Recallcited by 291
9 more
  1. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  2. 2018Analyzing Uncertainty in Neural Machine Translationcited by 126
  3. 2021Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Basescited by 107
  4. 2021BERTese: Learning to Speak to BERTcited by 82
  5. 2019Inducing Relational Knowledge from BERTcited by 75
  6. 2017Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddingscited by 59
  7. 2021Word Acquisition in Neural Language Modelscited by 41
  8. 2021P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Promptscited by 22
  9. 2020Inferring symmetry in natural languagecited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 3.

5 papers.

  1. 2023Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Modelscited by 32
  2. 2024The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMscited by 7
  3. 2023Exploring Lottery Prompts for Pre-trained Language Modelscited by 5
  4. 2024GPTDrawer: Enhancing Visual Synthesis through ChatGPTcited by 2
  5. 2024Enhancing Fact Retrieval in PLMs through Truthfulnesscited by 1