Audio note: this article contains 449 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Based off research performed in the MATS 5.1 extension program, under the mentorship of Alex Turner (TurnTrout). Research supported by a grant from the Long-Term Future Fund. TLDR: I introduce a new framework for mechanistically eliciting latent behaviors in LLMs. In particular, I propose deep causal transcoding - modelling the effect of causally intervening on the residual stream of a deep (i.e. _gtrsim10_-layer) slice of a transformer, using a shallow MLP. I find that the weights of these MLPs are highly interpretable -- input directions serve as diverse and coherently generalizable steering vectors, while output directions induce predictable changes in model behavior via directional ablation. Summary I consider deep causal transcoders (DCTs) with various activation functions [...] --- Outline: (05:40) Introduction (07:16) Related work (09:59) Theory (18:28) Method (22:04) Fitting a Linear MLP (25:08) Fitting a Quadratic MLP (30:12) Alternative formulation of tensor decomposition objective: causal importance minus similarity penalty (34:03) Fitting an Exponential MLP (36:35) On the role of _R_ (37:37) Relation to original MELBO objective (38:54) Calibrating _R_ (41:44) Case Study: Learning Jailbreak Vectors (41:49) Generalization of linear, quadratic and exponential DCTs (51:49) Evidence for multiple harmless directions (56:00) Many loosely-correlated DCT features elicit jailbreaks (59:44) Averaging doesnt improve generalization when we add features to the residual stream (01:01:05) Averaging does improve jailbreak scores when we ablate features (01:03:15) Ablating (averaged) target-layer features also works (01:04:49) Deeper models: constant depth horizon (_t-s_) suffices for learning jailbreaks (01:09:32) Application: Jailbreaking Representation-Rerouted Mistral-7B (01:17:15) Application: Eliciting Capabilities in Password-Locked Models (01:18:42) Future Work (01:19:01) Studying feature multiplicity (01:20:03) Quantifying a broader range of behaviors (01:20:24) Effect of pre-training hyper-parameters (01:22:05) Acknowledgements (01:22:16) Appendix (01:22:19) Hessian auto diff details The original text contained 26 footnotes which were omitted from this narration. The original text contained 2 images which were described by AI. --- First published: December 3rd, 2024 Source: https://www.lesswrong.com/posts/fSRg5qs9TPbNy3sm5/deep-causal-transcoding-a-framework-for-mechanistically --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.