---
title: "LM Alignment"
type: concept
related: [Monte Carlo Tree Search]
source: https://www.jemoka.com/posts/kbhlm_alignment/
confidence: high
status: active
---

Been Kim
alignment problem involves &ldquo;aligning&rdquo; the representation spaces between machines of the world and that of the human. alternative perspective: teach humans new concepts to understand/communicate better
feature attribution doesn&rsquo;t work We take that perspective because many of the intersectional intepretability doesn&rsquo;t work well (feature permutation, etc.)&mdash;feature attribution type analyses (&ldquo;Impossibility Theorems Been Kim&rdquo;) actually has no correlation with predictive results.
feature information store in models is unrelated to model edit success i.e.: knowledge storing location located using ROME technique, though it gives you a sense of the location to store information, doens&rsquo;t correlate to success of model editing.
can we use ML to teach people? for instance, we can teach grandmasters to play chess using AlphaGo, and see if we can make a quantitative impact.
concept A concept is a unit of knowledge that&rsquo;s useful for a task. Two properties:
minimality: irrelavent information has been removed transferable: it can be taught atomically filtering for good concepts Representing a concept as a sparse vector as the latent space. We can check if a concept is transferable by teaching a student agent by doing KL divergence.
demonstration learning instead of doing demonstration learning on machines, do it on HUMANS. Filter for the concepts that are well operationalized.
AlphaZero recap: using a dense network to embed the network, and then MCTS.
