mlsanityData to 13 Oct 2026

A Few-shot Approach to Resume Information Extraction via Prompts

arXiv 2209.09450 ↗First posted 20 Sep 2022cs.CLcited by 4

Not on the map. Why?

Abstract (arXiv)

Prompt learning's fine-tune performance on text classification tasks has attracted the NLP community. This paper applies it to resume information extraction, improving existing methods for this task. We created manual templates and verbalizers tailored to resume texts and compared the performance of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt template design across NLP tasks. We present the Manual Knowledgeable Verbalizer (MKV), a rule for constructing verbalizers for specific applications. Our tests show that MKV rules yield more effective, robust templates and verbalizers than existing methods. Our MKV approach resolved sample imbalance, surpassing current automatic prompt methods. This study underscores the value of tailored prompt learning for resume extraction, stressing the importance of custom-designed templates and verbalizers.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 19 of its 20 references that are in the map. First posted on arXiv 20 Sep 2022.

Builds on

19 of 20 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,106
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,549
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
  5. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,898
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,785
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,229
  8. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  9. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 935
  10. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 687
  11. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  12. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 605
7 more
  1. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 376
  2. 2021WARP: Word-level Adversarial ReProgrammingcited by 220
  3. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  4. 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
  5. 2020Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classificationcited by 98
  6. 2021Improving and Simplifying Pattern Exploiting Trainingcited by 97
  7. 2022Construction of English Resume Corpus and Test with Pre-trained Language Modelscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuningcited by 20
  2. 2024Challenges and Opportunities of NLP for HR Applications: A Discussion Papercited by 1
  3. 2023Resume Information Extraction via Post-OCR Text Processingcited by 0
  4. 2025Causal Synthetic Data Generation in Recruitmentcited by 0