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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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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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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