19 - Mechanistic Interpretability with Neel Nanda

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