Learning to Learn workshop at MLPrague 2023

This repo contains link to the materials for the Learning to Learn workshop on Machine Learning Prague 2023.

Getting started

The primary playground env for the exercises below is Google Colab. The linked Colab notebooks contain the resolution of dependences, but if you'd like to run the exercises elsewhere, simply install the attached requirements.txt into any environment:

git clone https://github.com/gaussalgo/L2L_MLPrague23.git
pip install -r L2L_MLPrague23/requirements.txt

Outline

1. Intro to Transformers

• Architectures

  • Difference to other arch's (attention layer)
  • Tasks (=objectives)

• Pre-training & Fine-tuning

• Inputs and outputs

  • Single token prediction

• Generation

  • Iterative prediction
  • Other generation strategies
  • [Hands-on] Constraining generated output (forcing & disabling)

2. In-context Learning and Few-shot Learning with Transformers

• Problem definition (usage)
• Contrast with Supervised ML
• Zero-shot vs few-shot
  • Examples
  • [Hands-on] comparison of zero-shot vs. few-shot performance (of some chosen ICL)

3. Methods for Improving ICL

Inference

• Demonstrations heterogeneity
• Prompt engineering
  • Promptsource - database of prompts?
  • [Hands-on] prompt engineering (inspired by the training data?)

Training Strategies

• Training strategies + existing models
  • training in explicit fewshot format (QA)
  • Instruction tuning
  • Multitask learning
  • Chain-of-Thought
  • Pre-training on a code
  • Fine-tuning with human feedback

Theory behind - why does ICL exist?

• Data properties fostering ICL
• Experiments
• Explanations of the existing models?

4. Hands-on in Improving Few-shot ICL

• [Hands-on] Customizing Few-shot ICL to specialized data
• Practical training pipeline
  • Overview of the training pipeline
  • Adaptor example

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Models evaluation & competition [Optional]

If you trained your own great few-shot ICL model, it would be a pity not to test it on some unseen reasoning tasks.

See the competition readme for how to evaluate the model and if it beats the baseline, how to spread the word!

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