princeton-nlp/LM-BFF

• highreadme#1

  Reposition the README H1 and opening paragraph to clarify its PEFT methodology

  Why:

  CURRENT

  # LM-BFF (**B**etter **F**ew-shot **F**ine-tuning of **L**anguage **M**odels)
  
  This is the implementation of the paper Making Pre-trained Language Models Better Few-shot Learners. LM-BFF is short for **b**etter **f**ew-shot **f**ine-tuning of **l**anguage **m**odels.

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  # LM-BFF: Parameter-Efficient Few-shot Fine-tuning of Language Models
  
  This is the official implementation of the ACL 2021 paper "Making Pre-trained Language Models Better Few-shot Learners" (https://arxiv.org/abs/2012.15723). LM-BFF (Better Few-shot Fine-tuning of Language Models) introduces a suite of simple and complementary parameter-efficient techniques for fine-tuning pre-trained language models on a small number of training examples, including prompt-based fine-tuning and refined in-context demonstrations.

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• mediumhomepage#2

  Add the arXiv paper link as the repository homepage

  Why:

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  https://arxiv.org/abs/2012.15723

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• lowtopics#3

  Expand repository topics to include related NLP and PEFT terms

  Why:

  CURRENT

  few-shot-learning, language-models, lm-bff

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  few-shot-learning, language-models, lm-bff, prompt-tuning, parameter-efficient-fine-tuning, peft, nlp, deep-learning

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I fine-tune pre-trained language models effectively with minimal training data?

  you: not recommended
  AI recommended (in order):

  1. LoRA
  2. QLoRA
  3. Prompt Tuning
  4. P-tuning v2
  5. AdapterHub
  6. NLTK
  7. spaCy
  8. Word2Vec
  9. GloVe
  10. GPT-4
  11. Claude 3
  12. Hugging Face Transformers Library
  13. BERT
  14. RoBERTa
  15. GPT-2
  16. Llama 2
  17. Mistral
  18. BioBERT
  19. PubMedBERT
  20. Hugging Face PEFT Library
  21. bitsandbytes
  22. Google Translate API
  23. LibreTranslate
  24. OpenAI API
  25. Anthropic API
  26. Google Gemini API
  27. Weights & Biases (W&B)
  28. MLflow

  AI recommended 28 alternatives but never named princeton-nlp/LM-BFF. This is the gap to close.

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• CATEGORY QUERY

  What techniques exist for enhancing few-shot learning capabilities of large language models?

  you: not recommended
  AI recommended (in order):

  1. FLAN
  2. T0
  3. LoRA
  4. Prefix-Tuning
  5. Prompt Tuning
  6. MAML
  7. Reptile
  8. REALM
  9. Dense Passage Retrieval (DPR)

  AI recommended 9 alternatives but never named princeton-nlp/LM-BFF. This is the gap to close.

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• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of princeton-nlp/LM-BFF?

  pass
  ✓AI named princeton-nlp/LM-BFF explicitly

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• If a team adopts princeton-nlp/LM-BFF in production, what risks or prerequisites should they evaluate first?

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  ✓AI named princeton-nlp/LM-BFF explicitly

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• In one sentence, what problem does the repo princeton-nlp/LM-BFF solve, and who is the primary audience?

  pass
  ✓AI named princeton-nlp/LM-BFF explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?