Prompting Methods with Language Models and Their Applications to Weak Supervision by Ryan Smith | Summary and Q&A

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January 19, 2022
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Snorkel AI
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Prompting Methods with Language Models and Their Applications to Weak Supervision by Ryan Smith

TL;DR

This content discusses the concept of prompting methods with language models and their applications to weak supervision, highlighting the benefits and challenges.

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Key Insights

  • Language models have advanced significantly, and their output can be used for various downstream tasks with transfer learning.
  • The current standard approach involves training a language model on a large amount of text and adding a task-specific classifier at the end.
  • Prompting is an alternative approach where the entire language model is used, and the desired output is obtained by giving it the right natural language context.
  • Prompting is useful when the number of training examples is low and when the label space contains encoded information.
  • Prompt engineering involves manually or automatically generating prompt templates to improve performance.
  • Answer engineering focuses on defining the answer space and mapping the answers back to the label space.
  • Weak supervision can be incorporated into prompt and answer engineering, allowing for the injection of domain knowledge to improve performance.
  • Training subject matter experts to write prompts could be useful if prompts are shown to have a measurable performance impact.

Transcript

so hello everyone it is my pleasure to introduce ryan uh ryan is an applied research scientist here at snorkel and he works on the research team with a focus on applying language models to weak supervision so without further ado please join me in welcoming ryan hey everyone um welcome to today's ml whiteboard so like roberto said my name is ryan sm... Read More

Questions & Answers

Q: How do prompting methods address the limitations of the standard approach to using language models in downstream tasks?

Prompting methods overcome limitations by allowing the language model to remain intact and leveraging natural language context for better performance. This reduces the need for large amounts of labeled data and preserves semantic meaning in the label space.

Q: How does the selection of pre-training objectives impact the effectiveness of prompting methods?

Different pre-training objectives, such as next token prediction and masked token prediction, impact how prompts are designed and the types of answers that can be included in the context. The choice of pre-training objective determines the capabilities and limitations of the language model for prompted prediction.

Q: What are the main components involved in prompt engineering?

Prompt engineering involves defining the prompt function and selecting the best filled prompt. Manual templates, automated templates, and ensemble methods are some approaches used in prompt engineering. The goal is to tailor the prompts to the specific task and leverage weak supervision to improve performance.

Q: How does answer engineering contribute to the effectiveness of prompting methods?

Answer engineering involves defining the shape and content of the answer space and mapping it back to the label space. By encoding domain knowledge into the answer space, prompting methods can better align the language model's predictions with the desired labels. Answer engineering is a critical step for leveraging weak supervision in prompting.

Summary & Key Takeaways

  • Language models, such as BERT and GPT, trained on large amounts of text, can be used for downstream tasks.

  • The standard approach is to train a task-specific classifier by attaching it to the trained language model.

  • However, prompting methods, which involve leaving the language model intact and providing natural language context, offer advantages like fewer training examples and the preservation of semantic meaning in the label space.

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