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One Sentence Abstract

"LM-BFF is presented as a task-agnostic method for few-shot learning, significantly improving upon standard fine-tuning procedures on various NLP tasks, through prompt-based fine-tuning and a novel pipeline for automating prompt generation, and a refined strategy for dynamically incorporating demonstrations into each context."

Simplified Abstract

Researchers developed a new and improved technique called LM-BFF (Better Few-Shot Fine-Tuning of Language Models) to help computers understand and learn from a small amount of information, like when you teach a new task to a language model using just a few examples. This method is like having a toolbox with helpful techniques to improve the learning process.

The LM-BFF method includes two main parts:

1. Prompt-based fine-tuning: This is like giving the computer a helpful hint or tutorial to understand the new task. The researchers also created a system that automatically creates these hints, making it easier and more efficient.
2. Refined strategy for demonstrations: This is like carefully selecting and incorporating a few examples to help the computer learn the new task. By doing this, the computer can learn faster and more accurately.

The researchers tested the effectiveness of their new method using a variety of tasks like classification and regression. The results showed that LM-BFF outperforms standard methods in situations where there are limited resources and information, achieving up to a 30% improvement and an average of 11% improvement across all tasks. This means that LM-BFF can be a helpful tool for various tasks, regardless of the specific domain or the amount of information available.

The LM-BFF implementation is available for anyone to use, making it easier for researchers to apply this innovative method to their work and improve the accuracy and reliability of their findings.

Study Fields

Main fields:

• Natural Language Processing (NLP)
• Machine Learning (ML)
• Language Models

Subfields:

• Few-shot learning
• Fine-tuning
• Prompt-based fine-tuning
• Automated prompt generation
• Demonstration incorporation strategies
• Task-agnostic methods
• Performance evaluation
• Classification and regression

Study Objectives

• Investigate few-shot learning in a practical scenario using smaller language models for which fine-tuning is computationally efficient.
• Develop LM-BFF: a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples, including:
  • Prompt-based fine-tuning with a novel pipeline for automating prompt generation.
  • A refined strategy for dynamically and selectively incorporating demonstrations into each context.
• Conduct a systematic evaluation to analyze few-shot performance on a range of NLP tasks, such as classification and regression.
• Compare the performance of LM-BFF against standard fine-tuning procedures in a low resource setting, aiming for up to 30% absolute improvement, and 11% on average across all tasks.
• Ensure that the approach is task-agnostic and makes minimal assumptions on task resources and domain expertise, making it a strong method for few-shot learning.
• Make their implementation publicly available (https://github.com/princeton-nlp/LM-BFFLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.).

Conclusions

• The study investigates few-shot learning in practical scenarios using smaller language models, making fine-tuning computationally efficient.
• The authors present LM-BFF, a suite of techniques that include prompt-based fine-tuning with an automated prompt generation pipeline, and a refined strategy for incorporating demonstrations dynamically and selectively.
• The researchers systematically evaluate the few-shot performance on a range of NLP tasks, including classification and regression.
• LM-BFF achieves up to a 30% absolute improvement and an 11% average improvement across all tasks compared to standard fine-tuning procedures, demonstrating its effectiveness in the low resource setting.
• The approach is task-agnostic and requires minimal assumptions on task resources and domain expertise, making it a strong method for few-shot learning in practical scenarios.
• The implementation of LM-BFF is publicly available at https://github.com/princeton-nlp/LM-BFFLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat..

References