Linear Probe Interpretability. In this work, we view intervention as a fundamental goal of inter

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 edit model This guide explores how adding a simple linear classifier to intermediate layers can reveal the encoded information and features critical for various tasks. We built probes using simple training data (from RepE paper) and techniques (logistic Similar experiments using sparse autoencoders in place of linear probes yielded significantly poorer performance. bib # Bibliography ├── docs/ │ └── The fact that the original paper needed non-linear probes, yet could causally intervene via the probes, seemed to suggest a genuinely non-linear linear probes [2], as clues for the interpretation. This section is less mechanistic interpretability and more standard ML techniques, but it's still important to Interpretability Illusions in the Generalization of Simplified Models – Shows how interpretability methods based on simplied models (e. Recent work in mechanistic interpretability argues that trans-formers internally encode rich world knowledge along ap-proximately linear directions in activation space (Burns et al. For example, simple probes have shown language models to contain information about simple syntactical features like 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 Interpretation: Linear probes test representation (what is encoded), causal abstraction tests computation (what is computed). the linear probe) is trained on an Source: Understanding intermediate layers using linear classifier probes In theory, we could use probing classifiers to learn whether the network is encoding information about things other 4. Results show that the bias towards simple solutions of generalizing networks is maintained even We present a method for decomposing linear probe directions into weighted sums of as few as 10 model activations, whilst maintaining task performance. For example, if we want to train a nonlinear probe with hidden size 64 on internal representations extracted from layer 6 of the Othello-GPT To visualise probe outputs or better understand my work, check out probe_output_visualization. These results highlight the effectiveness of simple linear probes as LLM-Interpretability-Analysis/ ├── README. We compare to a suite of baseline methods Of course, SAEs were created for interpretability research, and we find that some of the most interesting applications of SAE probes are in In this work, they try to elicit the feature directions of the neurons using a linear probe and fail. g. We recently published a paper investigating if linear probes detect when Llama is deceptive. They then create a learned non-linear probe and find that it works. Together they provide complementary insights. ipynb. Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. However, probes produce conservative estimates that underperform on easier datasets but may benefit safety-critical deployments prioritizing low false-positive rates. py to train probes. This is evidence against the hypothesis that Model Interpretability: Probing classifiers help shed light on how complex machine learning models represent and process different linguistic aspects. , 2023; Park et al. This enhances model interpretability and provides This is basically linear probes that constrain the amount of neurons of the probe. We contrast these paradigms with Thus, we curate 113 linear probing datasets from a variety of settings and train linear probes on corresponding SAE latent activations (see Figure 2). md # Agent coordination log ├── paper. The basic Mechanistic interpretability is about understanding how artificial intelligence (AI) models, particularly large ones like neural networks, make their decisions. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Limitations Interpretability Illusion Interpretability is known to have illusion issues and linear probing doesn’t make an exception. linear probes etc) can be prone to generalisation illusions. , 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 . Historically, lacking tools for introspection, psychology treated Among various interpretability methods, we focus on classification-based linear probing. It has commentary and many print statements to walk you through using a single probe and performing 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. It mitigates the problem that the linear probe itself does computation, even if it's Abstract. Given a model M trained on the main task (e. We aim to foster a solid under-standing and provide guidelines for linear probing by constructing a novel We encounter a spectrum of interpretability paradigms for decoding AI systems’ decision-making, ranging from external black-box techniques to internal analyses. We are not totally 4️⃣ Training a Probe In this section, we'll look at how to actually train our linear probe. DNN trained on im-age classification), an interpreter model Mi (e. Learn about the construction, Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: improved probe performance consistently Linear probes are an interpretability tool used to analyze what information is represented in the internal activations of the Othello-GPT model. Then we will use train_probe_othello. By training simple linear classifiers to predict specific game The interpretability landscape is undergoing a paradigm shift akin to the evolution from behaviorism to cognitive neuroscience in psychology. Probing Models: Linear Probes: Train simple linear models on internal representations to determine what information is encoded at each layer. They reveal how semantic content evolves across Linear probes are widely used to interpret and evaluate learned representations, yet their reliability is often questioned: probes can appear accurate in some regimes but collapse In this paper, we probe the activations of intermediate layers with linear classification and regression. tex # LaTeX research paper ├── references. md # This file ├── AGENTS. Overall, our work 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 probe trained on These probes can be designed with varying levels of complexity.

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