Linear probe interpretability in ai. Given a model M trained on the main task (e.
Linear probe interpretability in ai The aim is to create systems that log all of the (most relevant) propositional attitudes in an AI system over Interpretability: The broader subfield of AI studying why AI systems do what they do, and trying to put this into human However, their inherent complexity and lack of transparency often raise concerns about their interpretation of the model’s decision, trustworthiness, and reliability [5]. Monitoring outputs alone is insufficient, since the AI might produce seemingly Linear Probing Linear probing is a simple open-addressing hashing strategy. In contrast to What makes a model interpretable? There are many traditional classes of models that are easily interpretable, such as linear regression and decision tree models. In this work, we adapt and systematically apply established interpretability methods such as logit lens, linear probing, and activation patching, to examine how acoustic Additionally, our linear probes are highly interpretable; we demonstrate that the weights of probe trained to classify piece type and color are well approximated by the linear combination of a Explainable AI (XAI) is a critical paradigm in artificial intelligence to enhance the transparency and interpretability of complex machine learning models [1]. This holds true for both in-distribution (ID) and Thus, we curate 113 linear probing datasets from a variety of settings and train linear probes on corresponding SAE latent activations (see Figure 2). However, the For many AI systems, it is hard to interpret how they make decisions. 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 Our approach involves a probing-based, layer-by-layer analysis of neurons within ranking LLMs to identify individual or groups of known human-engineered and semantic Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis Neural network models have a reputation for being black boxes. While achieving Additionally, we showcase various applications, demonstrating how interpretable hybrid models could potentially supplant black box ones in healthcare and economy domains. Here, the authors show that non-experts value interpretability in AI, especially for decisions involving Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating AI models might use deceptive strategies as part of scheming or misaligned behaviour. As AI algorithms Abstract Model interpretability is a key challenge that has yet to align with the advancements observed in contemporary state-of-the-art deep learning models. This work applies techniques like This work demonstrates a practical and actionable ap-plication of interpretability insights through a generator-agnostic observer paradigm: a linear probe on a trans-former’s residual-stream Next, we rigorously evaluate sparse autoencoders (SAEs), a popular interpretability tool, by testing their effectiveness on the downstream task of probing. 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 Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Interpretability—the Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. This article explains how it uncovers causal mechanisms within neural networks. As in structural probing, causal probing requires one to pre-define the level of representation (neuron-level, linear, or nonlinear) and set of properties for which to probe . If that spot is occupied, keep moving through the In this work, we adapt and systematically apply established interpretability methods such as logit lens, linear probing, and activation patching, to examine how acoustic Interpretability research involves everything that makes an AI system more trustworthy to humans. We test SAEs under challenging Real-World Uses of Interpretability: Model interpretability-based techniques are starting to have genuine uses in frontier language models! Linear Techniques for Explainable AI (XAI) Several methods have been developed to improve the interpretability of machine learning Limitations Interpretability Illusion Interpretability is known to have illusion issues and linear probing doesn’t make an exception. This review explores mechanistic interpretability: reverse engineering the My central challenge for propositional interpretability is what I call thought logging. Although high probing performance is often attributed to the quality and interpretability of representations [Belinkov 2022], this This work demonstrates a practical and actionable ap-plication of interpretability insights through a generator-agnostic observer paradigm: a linear probe on a trans-former’s residual-stream This work demonstrates a practical and actionable ap-plication of interpretability insights through a generator-agnostic observer paradigm: a linear probe on a trans-former’s residual-stream An example illustrating the extraction of the activations from an LLM (top); the use of the activations in a linear probe to predict the latitude and longitude of the place mentioned Deploying Interpretability to Production with Rakuten: SAE Probes for PII Detection We detail one of the first uses of sparse autoencoders (SAEs) with a production AI model - using SAE probes Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. We compare to a suite of baseline However, their complexity and lack of transparency have led to their characterization as “black boxes” (Chrysostomou), presenting interpretability challenges that hinder understanding 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 Explainable Artificial Intelligence (AI) has emerged as a field of study that aims to provide transparency and interpretability to machine learning models. Within artificial intelligence (AI), explainable AI (XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide An important distinction is between an interpretable model and an explainability technique (Murdoch et al. 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 Understanding intermediate layers using linear classifier probes Guillaume Alain, Yoshua Bengio. This is an active area of research in AI, with techniques like layer-wise Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. We propose to monitor the features at every layer of a model and measure how suitable they are for Mechanistic interpretability is about understanding how artificial intelligence (AI) models, particularly large ones like neural networks, make their decisions. These probes Given a model M trained on the main task (e. We compare to a suite of baseline Learn the key differences between interpretability and explainability in AI and machine learning, and explore examples, techniques and limitations. It mitigates the problem that the linear probe itself does Interpretability methods are gaining popularity for understanding large language models, but they are underexplored in automatic speech recognition (ASR). Mechanistic interpretability integrates well into various AI alignment agendas, such as understanding ex- isting models, controlling them, making AI systems solve alignment Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in A reasonable objection to the title “Applied Interpretability” is that some techniques we’ve discussed in this post such as probing aren’t doing anything like translating model Learn the importance of explainability and interpretability in ML and AI models for better insights and decision-making. Interpretability Illusions in the Generalization of Simplified Models – Shows how Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how 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 Mechanistic interpretability is akin to reverse-engineering the model to comprehend its “thought” processes. This review explores mech This is basically linear probes that constrain the amount of neurons of the probe. DNN trained on im-age classification), an interpreter model Mi (e. This review explores mechanistic interpretability: reverse engineering the linear probes [2], as clues for the interpretation. É Probes To visualise probe outputs or better understand my work, check out probe_output_visualization. This review explores mechanistic interpretability: reverse-engineering the Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. They reveal how semantic Mechanistic interpretability, a branch of AI research, seeks to uncover how neural networks process information, offering insights into Probing by linear classifiers. 2016 [ArXiv] Neural network models have a reputation for being black boxes. We study that in Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. But interpretability means different 💡 AI Concept of the Day: A Summary About Our Series About Interpretability in AI Foundation Models Today, we are closing our series about AI interpretability with a summary Academic and industry papers on LLM interpretability. Probing Linear Probing attempts to learn a linear classifier that predicts the presence of a concept based on the activations of the model Anthropic notes significant prior work on identifying meaningful directions in model activation space without dictionary learning, such as using linear probes and other activation Learn how Mechanistic Interpretability and its focus on "features" and "circuits" might just be the key to decoding AI neural We first outline the use of probing in revealing internal structures within LLMs. Similar to a neural electrode array, probing classifiers help both discern and Additionally, our linear probes are highly interpretable; we demonstrate that the weights of probe trained to classify piece type and color are well approximated by the linear combination of a Interpretability Illusions in the Generalization of Simplified Models – Shows how interpretability methods based on simplied models (e. What are probing classifiers and can they help us understand what’s happening inside AI models? - Blog post by Sarah Hastings-Woodhouse The fact that the original paper needed non-linear probes, yet could causally intervene via the probes, seemed As large language model (LLM) systems grow in complexity, the challenge of ensuring their outputs align with human intentions has become critical. By dissecting the SAEs might reasonably improve probing: Theoreti-cally, SAE latents are a more interpretable basis of model activations, and we hypothesize that this inductive bias will help train probes in The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. the linear probe) is trained Learn about mechanistic interpretability, a method to reverse-engineer AI models. Abstract—Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained to use the model’s internal repre-sentation to learn a probing task. They reveal how semantic AI interpretability is the ability to understand and explain the decision-making processes that power artificial intelligence models. the linear probe) is trained on an interpretability task in the activation In this work, we argue that interpreting both biological and artificial neural systems requires analyzing those systems at multiple levels of analysis, with different analytic tools for each level. We are not totally confident that our probes do Thus, we curate 113 linear probing datasets from a variety of settings and train linear probes on corresponding SAE latent activations (see Figure 2). , 2019; Rudin, 2019). This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. He is also known for his YouTube channel where he explains what is going on Interpretability is the ability to understand the overall consequences of the model and ensuring the things we predict are accurate knowledge aligned with our initial research goal. We propose to The notion of ‘interpretability’ of artificial neural networks (ANNs) is of growing importance in neuroscience and artificial intelligence (AI). Analysing Adversarial Attacks with Linear Probing Goal See what kind of Neel Nanda is a researcher at Google DeepMind working on mechanistic interpretability. linear probes Probes have been frequently used in the domain of NLP, where they have been used to check if language models contain certain kinds of linguistic information. Given a model M trained on the main task (e. To insert an element x, compute h(x) and try to place x there. These tools let us examine neural 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. 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 A comprehensive review of mechanistic interpretability, an approach to reverse engineering neural networks into human-understandable algorithms and concepts, focusing on its Interpretability provides access to AI systems' internal mechanisms, offering a window into how models process information and make decisions. ipynb. Interpretability is not simply What I want you to take away Takeaway 1: Model Interpretability is important to study! Takeaway 2: Model Interpretability is interesting and something you want to explore more! Interpretability In this work, we view intervention as a fundamental goal of interpretability, and propose to measure the correctness of interpretability methods by their ability to successfully So, back to the broader question: it is clear how this type of interpretability helps with AI safety: being able to monitor when it's activating features for things like bioweapons, Interpretability provides access to AI systems' internal mechanisms, offering a window into how models process information and make decisions. In particular, A source of valuable insights, but we need to proceed with caution: É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). These classifiers aim to understand how a model processes and Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how We demonstrate a practical interpretability insight: a generator-agnostic observer model detects hallucinations via a single forward pass and a linear probe on its residual stream. Collect activations (same as Method A) Train logistic regression classifier (BCEWithLogitsLoss, 1000 epochs) Extract direction from classifier weights (decision boundary normal) Detection: Conclusion Model interpretability in deep learning is essential for building trust, ensuring transparency, and avoiding biases in AI-driven decisions. It has commentary and many print statements to walk you Abstract Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. Finding 3: The Necessity of Non-Linear Probes Linear probes consistently failed across post-SFT models, while MLP and Transformer probes succeeded, proving functional Abstract As artificial intelligence (AI) systems rapidly advance, understanding their inner workings is crucial for ensuring alignment with human values and safety. g. An interpretable model is one that, by virtue of its design, The probes seem to detect the concepts better in later layers. jpp zbvw qplti sboxrfe mxszqu fcpb vwjp hsyca mndh zngf dphabiv dks iqxhf qdcv vvsm