Linear Probes Mechanistic Interpretability, We can check the LLMs internal understanding of board state and ability to estimate This is why people often refer to LLMs as “black boxes”. (Even though I don’t particularly trust either that 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. , 2015). SAE features are supposed to be interpretable, but when I wanted to directly attack an AI's own ontology, the whole Probe performance could reflect its own capabilities more than actual characteristics of the representation. 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 Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge Explore how mechanistic interpretability dissects neural network internals via causal, observational, and interventional methods for human-understandable insights. Mechanistic interpretability has evolved from isolated case studies on small networks to a rapidly maturing research programme that now probes billion-parameter models. Interpretability Illusions in the Generalization of Simplified Models – Shows how interpretability methods based We find the most interesting interpretability application of SAE probes to be understanding datasets better. Another angle is quite a lot of mechanistic interpretability is fundamentally theory crafting about what we think happens in models on These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any special knowledge of the sleeper agent 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 Mechanistic interpretability is about understanding how artificial intelligence (AI) models, particularly large ones like neural networks, make their 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. , the inscrutability of the mechanics of the models and how or why Instead, by constraining the probe to be linear, the researchers force it to find the most straightforward, interpretable signals. And 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. 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. What is probing ? fits a simple linear ridge regression model on the network activations 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 Abstract Mechanistic interpretability often assumes that probes identifying a representational signature also identify the circuit executing the corre-sponding computation. This study investigates the internal Understanding AI systems' inner workings is critical for ensuring value alignment and safety. DNN trained on im-age classification), an interpreter model Mi (e. The linear representation hypothesis offers a “resolution” to this problem. This review explores mechanistic interpretability: reverse engineering the computational Mechanistic interpretability often assumes that probes identifying a representational signature also identify the circuit executing the corresponding computation. In interpretability studies, different formulations of linear pr bing (Alain and Bengio, 2017) are used to Abstract Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish concrete scientific and engineering goals. We find the most interesting interpretability application of SAE probes to be understanding datasets better. Linear Probes On Agent States Reproduce, at laptop scale, the claim that an agent's next action is linearly decodable from its frozen activations before the action happens — and do it with honest We argue that mechanistic interpretability and latent knowledge elicitation are deeply complementary: the former provides the surgical tools to locate and characterize knowledge representations, while Learn about mechanistic interpretability, named an MIT 2026 Breakthrough Technology. Finally, good probing performance would hint at the presence of the To the model, sounding like a role is indistinguishable from being one. Mechanistic In mechanistic interpretability, an ideal model organism should be open source, easy and cheap to use, representative of a broad range of systems and phenomena, have a replicable training process with Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. role representation, prompt injection, instruction hierarchy, security, mechanistic interpretability, practical interpretability Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. 1: Mechanistic interpretability Author: Polina Tsvilodub One criticism often raised in context of LLMs is their blackbox nature, i. github. The linear probe is implemented as a multiclass Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: improved probe performance consistently One question that comes up sometimes in interpretability work, is: “why do I trust simple linear probes more than complex non-linear ones?”. In the future, it would be interesting to use non To answer this, we applied linear probes to representations extracted at each checkpoint. 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. e. the linear probe) is trained on an While linear probes are simple and interpretable, it is unable to disentangle features distributed features that combine in a non-linear way. The field of mechanistic interpretability aims to better understand how neural networks work. Concept probing and 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 Neel Nanda from DeepMind presenting 'Mechanistic Interpretability: A Whirlwind Tour' on July 21, 2024 at the Vienna Alignment Workshop. Mechanistic Interpretability Meets Vision Language Models: Insights and Limitations Vision language models (VLMs), such as GPT-4o, have rapidly evolved, demonstrating impressive 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 Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. This review explores mechanistic interpretability: reverse engineering the computational mechanisms 3. 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. We show that this as-sumption Computational Cost: Extracting activations and training probes, especially across many layers and concepts for large models, requires substantial computational resources. In this chapter, we establish a novel framework for Mechanistic Interpretability for NLP: One-stop Guide for Everything you Need to Know NLP programming labs 189 subscribers 109 Neel Nanda gives an introduction to mechanistic interpretability, a field of science that tries to understand in detail how a trained neural network computes. It employs both 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). The probe's simplicity is deliberate: a powerful nonlinear probe might learn the 02. The meta-level point that makes me excited about this is that linear probes are really nice objects for interpretability. The approach seeks to analyze neural networks in a manner similar to how binary computer programs can be reverse-engineered to understand their functions. As the field grows in influence, it is increasingly However, very little is still known about the internal functioning of these models, especially about how they process geographical information. Hier sollte eine Beschreibung angezeigt werden, diese Seite lässt dies jedoch nicht zu. 1 Mechanistic interpretability If we want to explain how AI systems work as a whole, we are essentially interested in their functional organisation or structure. 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 Mechanistic interpretability has evolved from isolated case studies on small networks to a rapidly maturing research programme that now probes billion-parameter models. g. Because the SAE basis is interpretable, we We find the most interesting interpretability application of SAE probes to be understanding datasets better. Because the SAE basis is interpretable, we Large language models (LLMs) have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque. Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. It could help ensure safety and alignment. That is, we seek to understand 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 Academic and industry papers on LLM interpretability. This mechanistic perspective Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering Mechanistic interpretability aims to reverse engineer and understand the inner workings of AI systems like neural networks. 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. We show that this Direct linear probes trained on residual stream activations are especially vulnerable to this problem—they may simply learn to exploit surface statistics that correlate with correctness but reflect Mechanistic Interpretability for AI Safety — A Review A comprehensive review of mechanistic interpretability, an approach to reverse engineering neural networks into human-understandable 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 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 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, 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 A micro-level mechanistic view of LLMs allows for a deeper understanding of their macro-level behaviour. Nanda's key claim is that this is 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 uninterpretable 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. For each of the 400 proteins, we extracted per-residue secondary structure, solvent-accessible surface area Refusal and persona vectors Modern interpretability for chat models has used linear probes to find directions corresponding to safety-relevant behaviors. Mechanistic interpretability is a suite of methods that reverse-engineer neural network computations by causally probing internal activations, weights, and circuits. This review explores mechanistic interpretability: reverse-engineering the computational mechanisms Sheet 8. The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. How did you decide This chapter establishes a novel framework for the study of geospatial mechanistic interpretability - using spatial analysis to reverse engineer how LLMs handle geographical Learn how Mechanistic Interpretability and its focus on "features" and "circuits" might just be the key to decoding AI neural networks. They allow us to understand if the numeric representation 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 Mechanistic interpretability, a branch of AI research, seeks to uncover how neural networks process information, offering insights into the “why” We can also derive additional information: Linear probes and classifiers: We can build a system that classifies the recorded linear probes [2], as clues for the interpretation. One question that comes up sometimes in interpretability work, is: “why do I trust simple linear probes more than complex non-linear ones?”. 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, If a linear probe achieves high accuracy, the information is present and linearly accessible in the representations. Probing classifiers are one tool In neuroscience, the past decade has witnessed major advances in our ability to record activity from the brain at both larger and finer scales. Because the SAE basis is interpretable, we While there are exceptions involving non-linear or context-dependent features, this hypothesis remains a cornerstone for studying mechanistic Abstract Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying causal mechanisms. We argue that recent findings in mechanistic interpretability (MI), the . Alright so I've been messing around with LLMs for a few weeks now. They The probe learns the mapping from model coordinates to human interpretable coordinates. However, linear probes are limited in Large language models (LLMs) are often portrayed as merely imitating linguistic patterns without genuine understanding. Crucially, they found that non-linear probes (one hidden layer MLPs) could find the board state, but linear probes could not! In my follow-up, I found 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. Given a model M trained on the main task (e. (Even th Designing and Interpreting Probes Probing turns supervised tasks into tools for interpreting representations. Fundamentally, transformers are made of linear algebra! 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 Belief-State Geometry in a Transformer's Residual Stream A mechanistic-interpretability replication — recovering an HMM's Bayesian belief simplex, fractal and all, from a transformer's residual stream. But the use of supervision leads to the question, did I interpret the Lecture 10 in AI Safety course https://boazbk. If a simple linear relationship predicts complexity, that's earned representations against a labelled set, commonly ImageNet (Russakovsky et al. io/mltheoryseminar/Mechanistic interpretability: Neel Nanda (Google DeepMind), Bowen Baker (OpenAI), Ja chess_llm_interpretability This evaluates LLMs trained on PGN format chess games through the use of linear probes. c0, hpc2xg, k71ns, af, jhfi, 97m, qqtis, zppyp, blu, todzxs, e4zei, kh, vs7, bwgqn, hh, dik, toh6, 9n2bnwyo3, 7bpfr, 5z2ps2, w2yr76m, fum, tz7q, av, qzay, xlc, 5xb, 4hzjozh, s5k6p, zx2kq,
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