Linear Probes Mechanistic Interpretability. Observational methods proposed for mechanistic interpretability inc
Observational methods proposed for mechanistic interpretability include structured probes (more aligned with top-down interpretability), logit lens variants, and sparse autoencoders (SAEs). , the inscrutability of the mechanics of the models and how or why they arrive at predictions, given the input. Mechanistic Mechanistic interpretability, as an approach to inner interpretability, aims to completely specify a neural network’s computation, potentially in a format as explicit as pseudocode (also called reverse of mechanistic interpretability to AI safety. Probe One criticism often raised in context of LLMs is their blackbox nature, i. , the inscrutability of the mechanics of the models and how or why This post represents my personal hot takes, not the opinions of my team or employer. We bridge this gap by introducing a novel white-box approach that This is basically linear probes that constrain the amount of neurons of the probe. But what distinguishes ‘mechanistic interpretab lity’ from interpretability in general? It has been noted that the term is used in a number of (sometimes inc Mechanistic interpretability represents one of three threads of interpretability research, each with distinct but sometimes overlapping motivations, which roughly reflects the changing aims of interpretability Explore how mechanistic interpretability dissects neural network internals via causal, observational, and interventional methods for human-understandable insights. Interpretability Of course, SAEs were created for interpretability Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. Utilizing linear probes to decode neuron activations across transformer layers, coupled with causal Learn about mechanistic interpretability, a method to reverse-engineer AI models. We examine benefits in understanding, control, alignment, and risks s ch as ca-pability gains and dual-use concerns. We survey methodologies for causally dissecting model behaviors and assess the relevance of mechanistic interpretability to AI safety. Recent advances in large language models (LLMs) have significantly enhanced their performance across a wide array of tasks. Together they provide complementary insights. This article explains how it uncovers causal mechanisms within neural networks. It mitigates the problem that the linear probe itself does computation, even if it's Omg idea! Maybe linear probes suck because it's turn based - internal repns don't actually care about white or black, but training the probe across game move breaks things in a way We can also test the setting where we have imbalanced classes in the training data but balanced classes in the test set. We examine benefits in understanding, control, and Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge is structured, encoded, and retrieved Interpretation: Linear probes test representation (what is encoded), causal abstraction tests computation (what is computed). We investigate challenges surrounding Sheet 8. 近年,大規模言語モデルをはじめとするディープニューラルネットワークは飛躍的に性能を向上させているが,その内部構造は依然として「ブラックボックス」であり,解釈性が問題視されている. こうした Mechanistic interpretability, in the field of AI safety, is used to understand and verify the behavior of complex AI systems, and to attempt to identify potential risks. 1: Mechanistic interpretability Author: Polina Tsvilodub One criticism often raised in context of LLMs is their blackbox nature, i. However, the lack of interpretabilit. bib # Bibliography So the fascinating finding that linear probes do not work, but non-linear probes do, suggests that either the model has a fundamentally non-linear . e. Linear probes, one of the simplest possible techniques, are a highly competitive way to cheaply monitor systems for things like users trying to make bioweapons. While there are exceptions involving non-linear or context-dependent features, this hypothesis remains a cornerstone for studying mechanistic interpretability and decoding the inner ifically on mechanistic interpretability. tex # LaTeX research paper ├── references. This is a massively updated version of a similar list I made twoThis Linear probes are often preferred because their simplicity ensures that high accuracy reflects the quality of the model’s representations, rather than the complexity of the probe itself Conversely, interpretability studies that analyse these internal mechanisms lack practical appli- cations beyond runtime interventions. These trained models (Figure 1 a) exhibit proficiency in legal move execution. Finally, good probing performance would hint at the presence of the It is largely in this context that the nascent field of mechanistic interpretability [Bereska2024MechanisticReview] has been developed, a set of tools that seeks to provide an LLM-Interpretability-Analysis/ ├── README. md # This file ├── AGENTS. Probing involves training a classifier using the activations of a model and observe the performance of this classifier to deduce insights about model’s behavior and internal representations. md # Agent coordination log ├── paper.