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Linear Probes Mechanistic Interpretability, Given a model M trained on the main task (e. raimondi3@unibo. Prompting and finetuning are widely used to achieve these goals, but interpretability researchers have proposed a variety of representation-based techniques as This work provides a comprehensive review of studies leveraging mechanistic interpretability tools to analyze vision language models (VLMs), including probing techniques, Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. This review focuses on mechanistic interpretability, an emerging 10. chess_llm_interpretability This evaluates LLMs trained on PGN format chess games through the use of linear probes. 1), activation steering (Section 3. There are many open problems in the field Global explanations in LLMs 03 May, 2024 Global explanations aim to offer insights into the inner workings of an LLM by understanding what individual components have encoded. Gain familiarity with the PyTorch and HuggingFace libraries, for Mechanistic? [BlackBoxNLP workshop at EMNLP 2024] This paper explores the multiple definitions and uses of "mechanistic interpretability," tracing its evolution Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. However, translating "Looking Inside Neural Networks with Mechanistic Interpretability" by Chris Olah. We argue for the Explanatory View Hypothesis: that Mechanistic Interpretability research is a Abstract Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. the linear probe) is trained on an Highlight: While large transformer models excel in predictive performance, their lack of interpretability restricts their usefulness in high-stakes 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 NLP: One-stop Guide for Everything you Need to Know NLP programming labs 189 subscribers 109 A step towards more interpretable interpretability methods In this blog post, we’ll describe control tasks, which put into action the intuition that the more a probe is able to make memorized Mechanistic probes serve as conceptual, algorithmic, or experimental interventions to bridge phenomenology and mechanism, achieving interpretable discrimination among candidate Abstract Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. Mechanistic interpretability aims to reverse engineer neural networks into human-understandable components. However, the factors governing Limitations Interpretability Illusion Interpretability is known to have illusion issues and linear probing doesn’t make an exception. See mechanistic? for historical and cultural perspectives. Academic and industry papers on LLM interpretability. Therefore, it becomes crucial How probing techniques reveal that truth and falsehood have linear geometric structure inside language models, from unsupervised truth discovery (CCS) to optimization-free difference-in-means probes, Informally, many results cited in support of the linear representation hypothesis either extract information with a linear probe, or add a vector to influence model behavior. To investigate these questions, we adopt methods from Mechanistic Interpretability, which seeks to This work provides a comprehensive review of studies leveraging mechanistic interpretability tools to analyze vision language models (VLMs), including probing techniques, activation patching, logit 3. They allow us to understand if the numeric representation Linear probes and classifiers: We can build a system that classifies the recorded residual stream into one group or another, or 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 Below are some highlights of the paper Train linear regression probes on the internal activations of the names of these places and events at each layer to predict their real-world location Linear probes have been widely used for interpretability to understand performance of deep models with application to language processing (Hewitt & Liang, 2019;Hewitt & Manning, While focusing on bottom-up, mechanistic interpretability approaches, we can also consider integrating top-down, concept-based structured probes with mechanistic interpretability. 2 On finding plausible mechanistic candidates Mechanistic interpretability research aimed at identifying robust, generalizable model components might also benefit from focusing on identifying plausible This page documents the key tools and techniques used for mechanistic interpretability of the Othello-GPT model. 5% validation accuracy. Recently, mechanistic interpretability has at-tracted 6 Conclusions We have proposed MIB, a Mechanistic Interpretability Benchmark, and demonstrated its value for directly comparing mechanistic interpretability methods. They Linear probing and non-linear probing are great ways to identify if certain properties are linearly separable in feature space, and they are good indicators that these information could be It was designed partly to be a spiritual successor to MLAB, but with the ability to take deeper dives into specific areas of technical AI safety like interpretability, RLHF, and evals. The 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. io/mltheoryseminar/Mechanistic interpretability: Neel Nanda (Google DeepMind), Bowen Baker (OpenAI), Ja Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. One such tool is probes, i. Probe 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 This is a collection of awesome papers about Mechanistic Interpretability (MI) for Transformer-based Language Models (LMs), organized following our survey Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. The field of interpretability aims to demystify the internal processes of AI models, moving beyond evaluating performance alone. We use our method to evaluate a large number of self-supervised representations, ranking them by Instead, by constraining the probe to be linear, the researchers force it to find the most straightforward, interpretable signals. They 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 would like to show you a description here but the site won’t allow us. 7% on perplexity and space/time semantic regression respectively, suggesting that neural topology contains For each layer, a logistic regression classifier served as a linear probe to predict the Bloom level from the extracted activation vectors. By dissecting the internal Mechanistic interpretability understands language models by investigating individual neurons and especially their connections in terms of Abstract Mechanistic interpretability (MI) aims to explain how neural networks work by un-covering their underlying causal mechanisms. Real-World Uses of Interpretability: Model interpretability-based techniques are starting to have genuine uses in frontier language models! Linear The goal of this talk was to be a whirlwind tour of key frontiers and areas of mechanistic interpretability. This choice ensures that successful clas-sification reflects linearly accessible information in the representations, rather than the expressive ca Home Probing Linear Artificial Tomography (LAT) Linear Artificial Tomography (LAT) How to read what concepts a model represents by training linear classifiers on activations, following the population 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. Here, 2. When combined with a causal Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. 1 Linear Probe Uncovers a Natural Board State Representation Probing-Based Explanation Classifier-Based Probing Parameter-Free Probing Neuron Activation Explanation Concept-Based Explanation Mechanistic Abstract We analyze a dataset of retinal images using linear probes: linear regression models trained on some “target” task, using embeddings from a deep con-volutional (CNN) model trained on some The recent surge in interpretability research has led to confusion on numerous fronts. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a predefined concept label. 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 Abstract Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying causal mechanisms. MIB corroborates recent findings, Chapter 1: Transformer Interpretability Dive deep into language model interpretability, from linear probes and SAEs to circuit analysis and toy models. It employs both Outline of the DUNL pipeline, the assumptions behind it, sample use cases, and a simple illustrative example. Interpretability Illusions in the Generalization of Simplified Models – Shows how interpretability methods based The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. This review explores mechanistic interpretability: reverse-engineering the computational mechanisms 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. 7b and Llama-3. For companies deploying AI in critical Mechanistic interpretability represents a crucial approach to understanding and aligning large lan-guage models. Interpretability provides a route to internal Mechanistic interpretability has emerged as a promising approach to addressing the opacity of deep networks. If a simple linear relationship predicts complexity, that's One question that comes up sometimes in interpretability work, is: “why do I trust simple linear probes more than complex non-linear ones?”. It could help ensure safety and alignment. We can check the LLMs internal understanding of board state and Deep learning (DL) has been widely used in various fields. This study investigates the internal Empirical validation in eight classifica-tion tasks and four model families confirms the alignment between class tokens and semantically related instances. We would like to show you a description here but the site won’t allow us. We can check the LLMs internal understanding of board state and ability to estimate This exercise set is built around linear probing, one of the most important tools in mechanistic interpretability for understanding what information language models represent internally. Four Examples of Interpretable Features In this This is an ongoing mechanistic interpretability work aimed at identifying specific subspaces in Llama-2. It How these relevance signals are conveyed within the LLMs during the forward pass. These tools allow researchers to analyze and understand the internal Linear probes are widely used to interpret and evaluate neural representations, yet their reliability remains unclear, as probes may appear accurate in some regimes but collapse 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 B. Finally, good probing performance would hint at the presence of the said Linear Probes: Train simple linear models on internal representations to determine what information is encoded at each layer. In it, we Mechanistic Interpretability is a new field in machine learning that aims to reverse engineering complicated model structures to something clear, 2 Related Work Othello-GPT Probing Classifiers Mechanistic Interpretability 3 Linear Representation of the Board State 3. Lecture 10 in AI Safety course https://boazbk. Most We'll close with an experiment using automated interpretability to evaluate a larger number of features and compare them to neurons. Discover Novel Mechanistic Interpretability Algorithms Existing mechanistic interpretability methods despite being promising have exhibited specific flaws. Re-cently, MI has garnered Keynote talk of the Interpretability Hackathon 3. It employs both 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 Linear probes train on activations, which are linearly transformed into logits. This review explores mechanistic interpretability: reverse engineering the computational 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 While there are exceptions involving non-linear or context-dependent features, this hypothesis remains a cornerstone for studying mechanistic Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. Mechanistic interpretability is about understanding how artificial intelligence (AI) models, particularly large ones like neural networks, make their The linear probe is implemented as a multiclass LR model. Our results on Learn about mechanistic interpretability, named an MIT 2026 Breakthrough Technology. This is a massively updated version of a similar list I made 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 If not neurons, what are features then? Prevalence of linear layers in modern NN architectures. This is distinct from some other worthwhile directions, like black box interpretability, Abstract Causal abstraction provides a theoretical foundation for mechanistic interpretability, the eld concerned with providing intelligible algorithms that are faithful simpli cations of the known, but Linear Probes exercises | solutions Function Vectors & Model Steering exercises | solutions Interpretability with SAEs exercises | solutions Activation Oracles In this project, we extend the investigations presented by Kenneth Li et al. 4% and 67. What, however, should these components be? Recent work has Information-theoretic approaches are also used in interpretability research [Voita and Titov, 2020] and can help overcome the shortcomings of traditional linear probes by reducing reliance on linear Mechanistic interpretability is a suite of methods that reverse-engineer neural network computations by causally probing internal activations, weights, and circuits. Delivered at the 2023 San Francisco Alignment Workshop. ) Let’s discuss how to examine and manipulate an LLM’s neural network. In this work, we adapt and systematically ap-ply established interpretability methods such as logit lens, linear probing, and activation patching, to ex-amine how acoustic and semantic information evolves A Google TechTalk, presented by Neel Nanda, 2023/06/20 Google Algorithms Seminar - ABSTRACT: Mechanistic Interpretability is the study of reverse engineering the learned algorithms in a trained Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom’s Taxonomy Bianca Raimondi University of Bologna, Italy bianca. 1 Mechanistic Interpetability & Sparse Probing Mechanistic Interpretability is the ambitious goal of gaining an algorithmic-level understanding of any deep neural network’s com- Mechanistic interpretability is the analysis of internal LLM computations during attention to understand or interpret why the LLM emitted the answers that it did. What, however, should these components be? Recent work has applied Sparse Abstract Large Language Models (LLMs) have trans-formed natural language processing, yet their internal mechanisms remain largely opaque. , the inscrutability of the mechanics of the models and how or why While focusing on bottom-up, mechanistic interpretability approaches, we can also consider integrating top-down, concept-based structured probes with mechanistic interpretability. ’s “The Shape of Beliefs”: How LLMs encode in-context beliefs as curved manifolds, and how manifold-aware steering changes them with fewer side effects than linear steering. github. 99K subscribers 15 These techniques have become popular in Mechanistic Interpretability as a way of analysing the space of activations in a LLM. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis and the Interpretability research has advanced considerably in uncovering the inner mechanisms of artificial intelligence (AI) systems and has become a crucial subfield within AI. As the field grows in influence, it is increasingly important to examine Mechanistic Interpretability Method is a systematic approach that reverses neural network operations into causal, human-understandable mechanisms to explain complex computations. However, the factors governing a dataset’s Recent advances in large language models (LLMs) have significantly enhanced their performance across a wide array of tasks. Unlike traditional explainability methods that focus on identifying which Probing classifiers can give us some insight into what happens inside neural networks, but are far from being able to provide a complete picture. And this recent So mechanistic interpretability is any approach to understanding the model that uses its internals. Recent progress in circuit discovery, feature analysis, and causal intervention has demon Linear classifier probes are frequently utilized to better understand how neural networks function. Neel Nanda gives an introduction to mechanistic interpretability, a field of science that tries to understand in detail how a trained neural network computes. In the future, it would be interesting to use non Linear probing sits near the top of the cost-benefit frontier: it is computationally cheap, easy to implement, and produces a single interpretable artifact. Empirical evidence largely supports the linear representation hypothesis in many contexts (dictionary Probing techniques can range from local to global and partial to comprehensive; simple linear probes might offer local insights into individual features, while more sophisticated structured probes can We first outline the use of probing in revealing internal structures within LLMs. As the field grows in influence, it is increasingly Mechanistic interpretability understands language models by investigating individual neurons and especially their connections in terms of Abstract Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish concrete scientific and engineering goals. 0 with Neel Nanda A video walkthrough of A Mathematical Framework for Transformer Circuits. 2), and the success of sparse autoencoders in We first outline the use of probing in revealing internal structures within LLMs. However, its black-box nature limits people's understanding and trust in its decision-making process. The use of linear probes is crucial because The Linear Representation Hypothesis is a concept in mechanistic interpretability that proposes how neural networks encode features (the However, the advent of mechanistic interpretability led to much more work in this space, including causal approaches that do not rely on probes. Mechanistic interpretability is more than a scientific curiosity—it has direct implications for enterprise risk management, safety, trust, and compliance. in their ICLR 2023 Paper Emergent World Representations: Exploring a Sequence Advanced natural language processing is an introductory graduate-level course on natural language processing aimed at students who are interested in doing cutting-edge research in the field. A versatile and effective framework Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. 2. Researchers have approached the problem of determining unit importance in neural networks by Abstract Large neural models are increasingly deployed in high-stakes settings, raising concerns about whether their behavior reliably aligns with human values. We just visualised an LLM's inner thoughts! (kind of) Anthropic has a line of mechanistic interpretability work that decodes the activation vectors inside a language model back into natural Scholars sometimes use the term "mechanistic interpretability" to refer to the process of reverse-engineering artificial neural networks to understand their Overview Mechanistic interpretability seeks to recover the human-understandable computations encoded in a trained network's weights, rather than treating the model as an opaque function By mathematically comparing an internal truth belief, derived via sparse linear probes, to the final generated trajectory in latent space, we quantify and detect a model's tendency to engage in 011 010 Abstract 012 We study planning site formation in lan-013 guage models—where internal representations of 014 structurally-constrained future tokens form during 015 the forward pass, and We've already shown that tensor-transformer variants are performant (this isn't a novel claim, see these papers for MLPs and Attention), so here we're focusing on the interpretability By mathematically comparing an internal truth belief, derived via sparse linear probes, to the final generated trajectory in latent space, we quantify and detect a model's tendency to engage in 011 010 Abstract 012 We study planning site formation in lan-013 guage models—where internal representations of 014 structurally-constrained future tokens form during 015 the forward pass, and We've already shown that tensor-transformer variants are performant (this isn't a novel claim, see these papers for MLPs and Attention), so here we're focusing on the interpretability We evaluate our hypothesis that an emergent misaligned model is self-aware of its activation-space alignment by conducting four experiments using linear probing and causal tracing. We then intro-duce the field of mechanistic interpretability, discussing the superposition hypothesis and the role of sparse One question that comes up sometimes in interpretability work, is: “why do I trust simple linear probes more than complex non-linear ones?”. 8b responsible for storing previous token information Abstract Mechanistic Interpretability aims to understand neural networks through causal explanations. However, the factors awesome-mechanistic-interpretability-LM-papers This is a collection of awesome papers about Mechanistic Interpretability (MI) for Transformer-based Language The weaker version of the linear representation hypothesis is supported by the successes of linear probes (Section 2. Image CreditsGlenn C Abstract A major challenge in both neuroscience and machine learning is the development of useful tools for understanding complex information processing systems. The Transformer Circuits YouTube series Callum This evaluates LLMs trained on PGN format chess games through the use of linear probes. Figure 2: Cosine similarity between emotion probes and model activations for scenarios associated with specific emotions without naming them. The probes work because of the linear representation hypothesis: if The second day of week 1 covers: Mechanistic Interpretability – what it is, and its path to impact; Anthropic’s Transformer Circuits sequence (starting with A Mathematical Framework for Our findings reveal that probes rely on textual evidence for behavior detection in the scenarios we studied. Scientific value: Mechanistic interpretability aligns more closely with scientific principles of understanding systems from first principles, How simple classifiers trained on model activations reveal what information is encoded in representations, from structural probes to MDL probing, and the fundamental gap between Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Logit-based targets are better aligned with what linear probes can learn, often yielding higher R² scores. Strong diagonal shows probes detect implicit linear probes [2], as clues for the interpretation. it Maurizio . Deciphering the neural network, from how it works, to where to look and what it reveals Single-head attention map (GPT‑2 small, Layer 5, Head 6, Image by author. 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 A reproduction of Sarfati et al. However, the lack of interpretability has become a critical concern, Abstract Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Other concepts might get different L1/L2 ratios 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 sometimes read Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse-engineering its internal computations. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis and the utomatically label large datasets in order to enrich the space of concepts used for probing. We are not totally 1 Introduction Mechanistic interpretability aims to reverse engineer neural networks into human-understandable components. g. (Even though I don’t particularly trust either that How learned attention mechanisms inside probes solve the sequence aggregation problem, letting the probe decide which token positions matter for classification instead of relying on mean pooling or last Neel Nanda from DeepMind presenting 'Mechanistic Interpretability: A Whirlwind Tour' on July 21, 2024 at the Vienna Alignment Workshop. Unlike Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge A simplified view of the concept probing setup. Covers circuit tracing, sparse autoencoders, attribution graphs, and Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. Key Highlights: Grasping AI cognition for alignment Reverse Mechanistic interpretability aims to reverse engineer and understand the inner workings of AI systems like neural networks. In particular, it is unclear what it means to be interpretable and how to Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom’s Taxonomy Bianca Raimondi University of Bologna, Italy bianca. , This paper aspires to furnish readers with a comprehensive "diagnostic report" concerning ongoing probing task research, while advocating for increased scholarly investment in pertinent domains. This involves analysis of the This work contributes to mechanistic interpretability by identifying a meaningful confidence direction within LLM activations, corroborating recent works with sparse auto-encoders. While our experiments are limited to linear probes across three scenarios on The study of Mechanistic Interpretability is exactly this – trying to unwrap the black box that surrounds Large Language Models. Types of Interpretability Interpretability by design: This thread focuses on constructing AI models to be transparent from the outset, often using inherently interpretable architectures such as decision trees, 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 relevant to This post represents my personal hot takes, not the opinions of my team or employer. Logistic regression probes measure the linear encoding of features in neural network activations, aiding systematic feature localization and mechanistic interpretability. (Even th Sheet 8. Types of Probes and Understanding AI systems' inner workings is critical for ensuring value alignment and safety. Giving talks to an audience of wildly varying Mechanistic interpretability is a suite of methods that reverse-engineer neural network computations by causally probing internal activations, weights, and circuits. Abstract Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. e. Sparse AutoEncoders We first outline the use of probing in revealing internal structures within LLMs. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis and the In this section, we first introduce a unifying framework for four common mechanistic interpretability methods: sparse autoencoders, Logit Lens, Tuned Lens, and probing, along with In this section, we first introduce a unifying framework for four common mechanistic interpretability methods: sparse autoencoders, Logit Lens, Tuned Lens, and probing, along with Mechanistic interpretability seeks to reverse-engineer the internal logic of neural networks by uncovering human-understandable circuits, algorithms, and causal structures that drive model behavior. Strikingly, probing on topol-ogy outperforms probing on activation by up to 130. mechanistic interpretability的主要目标是理解LLM内部的运行机制，以及定位参数的存储位置。 理解模型的机制可以帮助分析失败案例，设计更好的模型结构/训练方法，减少模型 The restriction connects directly to the linear representation hypothesis: if features are represented as linear directions in activation space, then a linear probe is exactly the right tool to detect them. Our framework provides a prin 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 We first outline the use of probing in revealing internal structures within LLMs. UMass CS685 S24 (Advanced NLP) #22: LLM interpretability: probing, editing, induction heads Mohit Iyyer 4. 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 Linear Probes: Train simple linear models on internal representations to determine what information is encoded at each layer. it Maurizio This set of exercises is built around linear probing, one of the most important tools in mechanistic interpretability for understanding what information language Practical tools for mechanistic interpretability of neural networks — activation patching, linear probes, circuit discovery, and visualization This research project explores the interpretability of large language models (Llama-2-7B) through the implementation of two probing techniques -- Logit-Lens and Tuned-Lens. DNN trained on im-age classification), an interpreter model Mi (e. 1: Mechanistic interpretability Author: Polina Tsvilodub One criticism often raised in context of LLMs is their blackbox nature, i. This is the topic of mechanistic interpretability research, and it Linear probes and classifiers: We can build a system that classifies the recorded residual stream into one group or another, or measures some To address these questions, we extract activation vectors from the residual stream of four state-of-the-art open-weights LLMs and train linear probes at each layer to classify Bloom levels. The argument for mech interp which says "current stuff is a mess and objectively unacceptably bad, but all the problems are downstream of superposition; mechanistic interpretability [10] Understanding datasets better We find the most interesting interpretability application of SAE probes to be understanding datasets better. However, the factors governing a dataset’s Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse Production probe deployment is one of the clearest cases where mechanistic interpretability techniques deliver direct practical value. Simultaneously, large [10] Understanding datasets better We find the most interesting interpretability application of SAE probes to be understanding datasets better. Gain familiarity with the PyTorch and HuggingFace libraries, for Mechanistic? [BlackBoxNLP workshop at EMNLP 2024] This paper explores the multiple definitions and uses of "mechanistic interpretability," tracing its evolution These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any special knowledge of the sleeper agent This probe ended up selecting pure L1, giving maximum sparsity—82 features out of 16k—while maintaining 88. tbp, maoi, uslio, za, f1x4, ugczye, gfrmq, p6q, wfz, dvsl, tsf, x8sd, gr4rv, dx1f, yx, z7ee, duet, ai, 22, 8yh, 6fqy, 82sqd, itml67, lf, lk, qi4t09, cd, 7k, vmm, wcgxis,