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Elicit Machine Learning Reading List

Purpose

The purpose of this curriculum is to help new Elicit employees learn background in machine learning, with a focus on language models. I’ve tried to strike a balance between papers that are relevant for deploying ML in production and techniques that matter for longer-term scalability.

If you don’t work at Elicit yet - we’re hiring ML and software engineers.

How to read

Recommended reading order:

  1. Read “Tier 1” for all topics
  2. Read “Tier 2” for all topics
  3. Etc

✨ Added after 2024/4/1

Table of contents

Fundamentals

Introduction to machine learning

Tier 1

Tier 2

Tier 3

Transformers

Tier 1

Tier 2

Tier 3

Tier 4+

Key foundation model architectures

Tier 1

Tier 2

Tier 3

Tier 4+

Training and finetuning

Tier 2

Tier 3

Tier 4+

Reasoning and runtime strategies

In-context reasoning

Tier 2

Tier 3

Tier 4+

Task decomposition

Tier 1

Tier 2

Tier 3

Tier 4+

Debate

Tier 2

Tier 3

Tier 4+

Tool use and scaffolding

Tier 2

Tier 3

Tier 4+

Honesty, factuality, and epistemics

Tier 2

Tier 3

Tier 4+

Applications

Science

Tier 3

Tier 4+

Forecasting

Tier 3

Search and ranking

Tier 2

Tier 3

Tier 4+

ML in practice

Production deployment

Tier 1

Tier 2

Benchmarks

Tier 2

Tier 3

Tier 4+

Datasets

Tier 2

Tier 3

Advanced topics

World models and causality

Tier 3

Tier 4+

Planning

Tier 4+

Uncertainty, calibration, and active learning

Tier 2

Tier 3

Tier 4+

Interpretability and model editing

Tier 2

Tier 3

Tier 4+

Reinforcement learning

Tier 2

Tier 3

Tier 4+

The big picture

AI scaling

Tier 1

Tier 2

Tier 3

Tier 4+

AI safety

Tier 1

Tier 2

Tier 3

Tier 4+

Economic and social impacts

Tier 3

Tier 4+

Philosophy

Tier 2

Tier 4+

Maintainer

[email protected]