Queue Composition Is Using Prior Information to Figure Previously Computed Information

How good are we at understanding the internal computation of advanced machine learning models, and do we have a hope at getting better? In this episode, Neel Nanda talks about the sub-field of mechanistic interpretability research, as well as papers he's contributed to that explore the basics of transformer circuits, induction heads, and grokking.

Topics we discuss, and timestamps:

 - 00:01:05 - What is mechanistic interpretability?

 - 00:24:16 - Types of AI cognition

 - 00:54:27 - Automating mechanistic interpretability

 - 01:11:57 - Summarizing the papers

 - 01:24:43 - 'A Mathematical Framework for Transformer Circuits'

   - 01:39:31 - How attention works

   - 01:49:26 - Composing attention heads

   - 01:59:42 - Induction heads

 - 02:11:05 - 'In-context Learning and Induction Heads'

   - 02:12:55 - The multiplicity of induction heads

   - 02:30:10 - Lines of evidence

   - 02:38:47 - Evolution in loss-space

   - 02:46:19 - Mysteries of in-context learning

 - 02:50:57 - 'Progress measures for grokking via mechanistic interpretability'

   - 02:50:57 - How neural nets learn modular addition

   - 03:11:37 - The suddenness of grokking

 - 03:34:16 - Relation to other research

 - 03:43:57 - Could mechanistic interpretability possibly work?

 - 03:49:28 - Following Neel's research

The transcript: axrp.net/episode/2023/02/04/episode-19-mechanistic-interpretability-neel-nanda.html

Links to Neel's things:

 - Neel on Twitter: twitter.com/NeelNanda5

 - Neel on the Alignment Forum: alignmentforum.org/users/neel-nanda-1

 - Neel's mechanistic interpretability blog: neelnanda.io/mechanistic-interpretability

 - TransformerLens: github.com/neelnanda-io/TransformerLens

 - Concrete Steps to Get Started in Transformer Mechanistic Interpretability: alignmentforum.org/posts/9ezkEb9oGvEi6WoB3/concrete-steps-to-get-started-in-transformer-mechanistic

 - Neel on YouTube: youtube.com/@neelnanda2469

 - 200 Concrete Open Problems in Mechanistic Interpretability: alignmentforum.org/s/yivyHaCAmMJ3CqSyj

 - Comprehesive mechanistic interpretability explainer: dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J

Writings we discuss:

 - A Mathematical Framework for Transformer Circuits: transformer-circuits.pub/2021/framework/index.html

 - In-context Learning and Induction Heads: transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html

 - Progress measures for grokking via mechanistic interpretability: arxiv.org/abs/2301.05217

 - Hungry Hungry Hippos: Towards Language Modeling with State Space Models (referred to in this episode as the "S4 paper"): arxiv.org/abs/2212.14052

 - interpreting GPT: the logit lens: lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens

 - Locating and Editing Factual Associations in GPT (aka the ROME paper): arxiv.org/abs/2202.05262

 - Human-level play in the game of Diplomacy by combining language models with strategic reasoning: science.org/doi/10.1126/science.ade9097

 - Causal Scrubbing: alignmentforum.org/s/h95ayYYwMebGEYN5y/p/JvZhhzycHu2Yd57RN

 - An Interpretability Illusion for BERT: arxiv.org/abs/2104.07143

 - Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small: arxiv.org/abs/2211.00593

 - Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets: arxiv.org/abs/2201.02177

 - The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models: arxiv.org/abs/2201.03544

 - Collaboration & Credit Principles: colah.github.io/posts/2019-05-Collaboration

 - Transformer Feed-Forward Layers Are Key-Value Memories: arxiv.org/abs/2012.14913

  - Multi-Component Learning and S-Curves: alignmentforum.org/posts/RKDQCB6smLWgs2Mhr/multi-component-learning-and-s-curves

 - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks: arxiv.org/abs/1803.03635

 - Linear Mode Connectivity and the Lottery Ticket Hypothesis: proceedings.mlr.press/v119/frankle20a