# thunlp/PromptPapers

Must-read papers on prompt-based tuning for pre-trained language models.

Repository: https://github.com/thunlp/PromptPapers
Canonical: https://ross.abutalabs.com/products/promptpapers
License Family: other
Topics: nlp, pre-trained-language-models, ai, machine-learning, bert, prompt-toolkit, prompt, prompt-learning, prompt-based
Last push: 2023-07-17T09:54:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1915, "days_push": 1143, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4326, forks 387 (observed 2026-08-28T04:08:42.059349+00:00)

## What it is
A curated must-read paper list on prompt-based tuning for pre-trained language models, maintained by THUNLP. It organizes research papers into categories like overview, basics, analysis, improvements, and specializations.

## Use cases
- find must-read papers on prompt-based tuning
- learn about prompt learning for pre-trained language models
- survey research on prompt tuning and prompt engineering
- get started with prompt-based fine-tuning research
- track the latest papers on prompt learning

## When to choose
- you need a curated reading list on prompt learning research
- you are a researcher surveying prompt-based tuning methods
- you want paper recommendations organized by topic and contribution

## When to avoid
- you need runnable code or a software toolkit (use OpenPrompt instead)
- you need a comprehensive database of all NLP papers rather than a curated selection
- you need up-to-date coverage after 2023

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning
- domain: large-language-models, tutorials
- platform: -
- tags: awesome-list, papers, prompt-learning, prompt-tuning, pre-trained-language-models, curated-list, natural-language-processing

## Member repositories
- thunlp/PromptPapers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:42.059349+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:41.931155+00:00, confidence not recorded.
  - readme: https://github.com/thunlp/PromptPapers (fetched 2026-08-28T04:08:42.059349+00:00, sha 79accf773713)
- Data as of 2026-08-30T08:39:29.467469+00:00.