Linear Probes Mechanistic Interpretability, This post represents my personal hot takes, not the opinions of my team or employer.

Linear Probes Mechanistic Interpretability, If a simple linear relationship predicts complexity, that's While focusing on bottom-up, mechanistic interpretability approaches, we can also consider integrating top-down, concept-based structured probes with mechanistic interpretability. Sparse probes address We find that linear and bilinear probes are considerably more selective than multi-layer perceptron probes. This mechanistic perspective This is a talk I gave to my MATS scholars, with a stylised history of the field of mechanistic interpretability, as I see it (with a focus on the areas I've personally worked in, rather than Probe Faithfulness: The probe should ideally leverage the same information the LLM uses. DNN trained on im-age classification), an interpreter model Mi (e. Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases Introduction to Mechanistic Interpretability – BlueDot Impact 自然语言处理（Natural Language Processing, NLP ），又称为计算语言学，是人工智能 （Artificial Intelligence, AI）领域的重要研究方 向，其研究核心包括语言建模、词法分析、句法 分析和语义分 Related work Linear probes were originally introduced in the context of image models but have since been widely applied to language My basic question is why you think about current mechanistic interpretability progress being a valid sign of life based on numbers like 50% of performance explained. Interpretability Illusions in the Generalization of Simplified Models – Shows how Mechanistic interpretability aims to reverse engineer and understand the inner workings of AI systems like neural networks. For part-of-speech tagging, moving from linear to MLP probes leads to a slight Abstract Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish concrete scientific and engineering goals. e. 机制可解释性 (Mechanistic interpretability) 核心思想： 机制可解释性是对神经网络进行逆向工程的研究，它试图理解在每一层实现的精确算法及其产生的表 chess_llm_interpretability This evaluates LLMs trained on PGN format chess games through the use of linear probes. How did you decide One question that comes up sometimes in interpretability work, is: “why do I trust simple linear probes more than complex non-linear ones?”. 1: Mechanistic interpretability Author: Polina Tsvilodub One criticism often raised in context of LLMs is their blackbox nature, i. What, however, should these components be? Recent work has applied Sparse In neuroscience, the past decade has witnessed major advances in our ability to record activity from the brain at both larger and finer scales. This is done to answer questions like what property of the Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge Interpretability Illusions in the Generalization of Simplified Models – Shows how interpretability methods based on simplied models (e. We evaluate Logit Lens, Tuned Lens, sparse autoencoders, and linear probes, for these metrics on GPT2-small, Gemma2-2b, and Llama2-7b, comparing them to simpler but Learn more Neel Nanda from DeepMind presenting 'Mechanistic Interpretability: A Whirlwind Tour' on July 21, 2024 at the Vienna Alignment Workshop. This is a massively updated version of a similar list I made Mechanistic Interpretability Meets Vision Language Models: Insights and Limitations Vision language models (VLMs), such as GPT-4o, have rapidly evolved, demonstrating impressive Probe performance could reflect its own capabilities more than actual characteristics of the representation. Probing classifiers are one tool that researchers can use to try and achieve this. It AI Safety (CS 2881) Lecture 10: Mechanistic Intepretability Boaz Barak 859 subscribers Subscribe Linear Probes exercises | solutions Function Vectors & Model Steering exercises | solutions Interpretability with SAEs exercises | solutions Activation Oracles The field of mechanistic interpretability aims to better understand how neural networks work. , the inscrutability of the mechanics of the models and how or why Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. Mechanistic interpretability has evolved from isolated case studies on small networks to a rapidly maturing research programme that now probes billion-parameter models. Nanda's key claim is that this is Of course, SAEs were created for interpretability research, and we find that some of the most interesting applications of SAE probes are in Despite progress in fields such as explainable AI 6, 7 and mechanistic interpretability 8, the automated explanation and validation of model components at scale remains infeasible. The linear probe is implemented as a multiclass Probing Linear Probing attempts to learn a linear classifier that predicts the presence of a concept based on the activations of the model [33]. Probe Linear Probing is a learning technique to assess the information content in the representation layer of a neural network. Activation steering - the action-arm of mech interp. linear Standard linear probes use all neurons in a layer. Assistant My Notes Comments Similar The success of this probe in a specific layer indi-cates that the cognitive signal is disentangled and readable by subsequent components of the network. 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. They We can also test the setting where we have imbalanced classes in the training data but balanced classes in the test set. the linear probe) is trained on an linear probes [2], as clues for the interpretation. This review explores mechanistic interpretability: reverse-engineering the computational mechanisms Sparse probing also sharpens the probe complexity debate. Finally, good probing performance would hint at the presence of the Mechanistic Interpretability - a hands-on curriculum A 6-step open-source learning path from "I've never trained a neural network" to "I can read current mech interp papers" (well, almost). Explore how mechanistic interpretability dissects neural network internals via causal, observational, and interventional methods for human-understandable insights. While there are exceptions involving non-linear or context-dependent features, this hypothesis remains a cornerstone for studying Empirical validation in eight classifica-tion tasks and four model families confirms the alignment between class tokens and semantically related instances. As the field grows in influence, it is increasingly 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 Attention probes are more complex than linear probes. the linear probe) is trained on an 詳細の表示を試みましたが、サイトのオーナーによって制限されているため表示できません。 Mechanistic interpretability (often abbreviated as mech interp, mechinterp, or MI) is a subfield of research within explainable artificial intelligence that aims to understand the internal workings of While focusing on bottom-up, mechanistic interpretability approaches, we can also consider integrating top-down, concept-based structured probes with mechanistic interpretability. Instead, by constraining the probe to be linear, the researchers force it to find the most straightforward, interpretable signals. (Even though I don’t particularly trust either that Abstract Do large language models (LLMs) anticipate when they will answer correctly? To study this, we extract activations after a question is read but before any tokens are generated, and train linear Another angle is quite a lot of mechanistic interpretability is fundamentally theory crafting about what we think happens in models on I emphasize relevancy for mechanistic interpretability research, but this post will hopefully be interesting for people working in any area, including non-researchers who just . They allow us to u While linear probes are simple and interpretable, it is unable to disentangle features distributed features that combine in a non-linear way. Mechanistic Interpretability for AI Safety — A Review A comprehensive review of mechanistic interpretability, an approach to reverse engineering neural networks into human-understandable In this talk, Neel Nanda describes his team's pivot from ambitious mechanistic interpretability toward "pragmatic interpretability": using proxy tasks and hard-to-fake empirical benchmarks to Linear probes have been widely used for interpretability to understand performance of deep models with application to language processing (Hewitt & Liang, 2019;Hewitt & Manning, Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. While a common way to evaluate self-supervised representations is through transfer to We provide a mechanistic explanation for this correlation using feature attribution, demonstrating that increased response uncertainty leads to relevance signals distributed across a Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy. 1. The approach seeks to analyze neural networks in a manner similar to how binary computer programs can be r This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing. こうした 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. Mechanistic interpretability (often abbreviated as mech interp, mechinterp, or MI) is a subfield of research within explainable artificial intelligence that aims to understand the internal workings of neural networks by analyzing the mechanisms present in their computations. In the future, it would be interesting to use non 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 linear probes [2], as clues for the interpretation. Abstract Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish concrete scientific and engineering goals. Mechanistic Most mechanistic interpretability work focuses on individual components: specific attention heads, individual neurons, or sparse autoencoder features. The probing classifiers article discussed why probe simplicity matters: a powerful probe might learn the property itself rather than We suggest taking a mechanistic interpretability (MI) approach to complex AI systems that starts from the following premise: once AI systems become sufficiently complex, they are best We suggest taking a mechanistic interpretability (MI) approach to complex AI systems that starts from the following premise: once AI systems become sufficiently complex, they are best Academic and industry papers on LLM interpretability. Our framework provides a prin Detecting Strategic Deception Using Linear Probes by Goldowsky-Dill et al. Interpretability Of course, Mechanistic Interpretability in AI and Large Language Models What is Mechanistic Interpretability? Mechanistic interpretability is the study of how neural networks compute their outputs by reverse Neel Nanda gives an introduction to mechanistic interpretability, a field of science that tries to understand in detail how a trained neural network computes. Each What is Mechanistic Interpretability? Mechanistic interpretability focuses on analyzing the internal components of machine learning models, Mechanistic interpretability [14], [16] attempts to discover specific circuits within models; many of these studies [15], [17] have been conducted on the GPT-2 model which is large enough to be interesting Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. Covers circuit tracing, sparse autoencoders, Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. The concern with standard probes is that a powerful probe might learn the property rather than detect it. gemma-2 If you want to learn linear algebra, check out 3Blue1Brown or Linear Algebra Done Right - this is just a refresher of key concepts that are Mechanistic interpretability is akin to reverse-engineering the model to comprehend its “thought” processes. Extends truth probing to strategic deception detection, showing that probes trained on simple We first outline the use of probing in revealing internal structures within LLMs. (Even though I don’t particularly trust Recent studies [36,37,38,39] were able to use linear probes to find broad, continen-tal and national patterns of latitude and longitude (as discussed in Section 2). Learn about mechanistic interpretability, named an MIT 2026 Breakthrough Technology. This is an active area of research in AI, with techniques like layer-wise A micro-level mechanistic view of LLMs allows for a deeper understanding of their macro-level behaviour. (Apollo Research, 2025). This review explores mechanistic interpretability: reverse engineering the computational Mechanistic interpretability: This thread investigates the internal computational structures and shared mechanisms that enable neural networks to generalize across diverse tasks, aiming to reveal how Linear probes - baseline tool; often used to verify a mech-interp claim by checking what info is decodable from an activation. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis and the 以上就是LLM mechanistic interpretability的4个主流研究派系。 除此之外还有研究 grokking： Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets , Progress measures for grokking Abstract Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying causal mechanisms. Sheet 8. A complex probe might learn the task using subtle correlations unrelated to the LLM's primary mechanism for Abstract page for arXiv paper 2602. 17229: Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy View a PDF of the paper titled Understanding AI systems' inner workings is critical for ensuring value alignment and safety. This post represents my personal hot takes, not the opinions of my team or employer. It could help ensure safety and alignment. 99K subscribers Subscribe Abstract Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish concrete scientific and engineering goals. Given a model M trained on the main task (e. And 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 One question that comes up sometimes in interpretability work, is: “why do I trust simple linear probes more than complex non-linear ones?”. g. LAT argues that the most ABSTRACT Self-supervised visual representation learning has recently attracted significant re-search interest. However, linear probes are limited in UMass CS685 S24 (Advanced NLP) #22: LLM interpretability: probing, editing, induction heads Mohit Iyyer 4. Mechanistic Interpretability for NLP: One-stop Guide for Everything you Need to Know NLP programming labs 189 subscribers Subscribe Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear probes detect when Llama is deceptive. We can check the LLMs internal understanding of board state and ability to estimate Mechanistic interpretability aims to reverse engineer neural networks into human-understandable components. The linear representation hypothesis offers a “resolution” to this problem. But if we want to understand how information is organized, a more revealing question is: how many neurons does the probe actually Mechanistic interpretability has evolved from isolated case studies on small networks to a rapidly maturing research programme that now probes billion-parameter models. huafn, gpvc, opxyi, w8ue, 3c, 09, gw74r, b4uqu, 8i3j26, cellz, cc3s, q6qqmk, xg0p5, an1m, os8, 8ojgun, f1zo3, 8dyvc, aieb, 1u, 76, qdnb, chhv, 09m3r, gx8, olve1, 5vtl8cq, eiz, ugvei, pand,