---
title: "与知识相结合的提示学习研究综述 — 鲍琛龙 2023"
description: "This Chinese-language survey from the National University of Defense Technology examines how prompt learning and knowledge can work together."
authors: ["鲍琛龙", "吕明阳", "唐晋韬", "李莎莎", "王挺"]
year: 2023
venue: "中文信息学报 (Journal of Chinese Information Processing)"
source: "http://jcip.cipsc.org.cn/article/id/zwxxxb_3555"
licence: "Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/bao-2023-prompt-learning-combined/"
---
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# 与知识相结合的提示学习研究综述

**Authors:** 鲍琛龙, 吕明阳, 唐晋韬, 李莎莎, 王挺 · **Year:** 2023 · **Venue:** 中文信息学报 (Journal of Chinese Information Processing) · **Licence:** Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报

**Links:** [Publisher's page](http://jcip.cipsc.org.cn/article/id/zwxxxb_3555) · [PDF](http://jcip.cipsc.org.cn/cn/article/pdf/preview/zwxxxb_3555.pdf)

## Summary
This Chinese-language survey from the National University of Defense Technology examines how prompt learning and knowledge can work together. It covers two directions: using prompts to extract knowledge, and using knowledge to build better prompts. Prompt learning recasts a downstream task in the format a pre-trained language model already handles, such as masked-token filling or generation. This avoids the cost and objective mismatch of full fine-tuning.

The authors first set out the core vocabulary, mostly following Liu et al.:
* template (prompt) engineering,
* answer engineering,
* multi-prompt strategies.

They then organise the literature into a taxonomy covering entity, relation and event extraction. A further category covers methods that inject external knowledge into prompt construction and label mapping. For each family of methods they note typical weaknesses, for example:
* error propagation when prompts are decomposed,
* the expense of hand-crafted templates,
* how to choose and order demonstrations.

The survey reports no new experiments. Its value is a structured map of an emerging area, especially for low-resource and few-shot extraction, together with a discussion of open limitations and future trends.

## Key points
- **Venue and pipeline:** Published in 中文信息学报 (Journal of Chinese Information Processing), vol. 37, no. 7, 2023, pp. 1–12. It frames prompt learning as three steps: template construction, answer search, and label-word mapping. Answers can be tokens, spans or sentences.
- **Entity extraction** is grouped into three families:
  - prompt decomposition: PLET and TemplateNER, where TemplateNER scores enumerated spans of up to 8 tokens with BART;
  - prefix prompts: plug-tagger and LightNER, which freeze the backbone and train inserted vectors;
  - demonstration-based methods: Lee et al. and EntLM.
- **Demonstrations (Lee et al.):** Entity-type-oriented demonstrations beat instance-oriented ones, which are retrieved with SBERT or BERTScore. Among the three ways of selecting examples (random, most frequent, search), the search strategy worked best.
- **Relation extraction** methods covered include:
  - typed entity markers (Zhou & Chen);
  - manual templates: AdaPrompt and RelationPrompt, the latter generating synthetic data for zero-shot relation extraction;
  - continuous templates with continuous label mapping: DART.
- **Event extraction and knowledge constraints:**
  - Event extraction largely follows the ACE2005 conventions. The surveyed methods include L2A for argument extraction, generation-based pipeline approaches, and prompt fusion (Lin et al.).
  - Knowledge-constrained prompting includes PTR and work by Hu, Chen and Ye et al. These methods use knowledge to shrink the search space.
  - Selective knowledge injection relies on a visible matrix to limit noise and heterogeneity.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{2023,
  title = {{与知识相结合的提示学习研究综述}},
  author = {鲍琛龙 and 吕明阳 and 唐晋韬 and 李莎莎 and 王挺},
  journal = {中文信息学报 (Journal of Chinese Information Processing)},
  year = {2023},
  url = {http://jcip.cipsc.org.cn/article/id/zwxxxb_3555}
}
```

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