✳flâneur — a map of the web's best reading

Trustworthy AI: Validity, Fairness, Explainability, and Uncertainty Assessments: Explainability methods: Linear Probes

carpentries-incubator.github.io · 3,747 words · saved by 1 readers

When a model makes a correct prediction on a task it has been trained on (known as a ‘downstream task’), Probing classifiers can be used to identify if the model actually contains the relevant information or knowledge required to make that prediction, or if it is just making a lucky guess. Furthermore, probes can be used to identify the specific components of the model that contain this relevant information, providing crucial insights for developing better models over time. A neural network takes its input as a series of vectors, or representations, and transforms them through a series of layers to produce an output. The job of the main body of the neural network is to develop representations that are as useful for the downstream task as possible, so that the final few layers of the network can make a good prediction. This essentially means that a good quality representation is one that already contains all the information required to make a good prediction. In other words, the feature

Trustworthy AI: Validity, Fairness, Explainability, and Uncertainty Assessments: Explainability methods: Linear Probes 29% Explainability methods: Linear Probes Last updated on 2024-12-16 | Edit this page Expand All Solutions Overview Questions How can probing classifiers help us understand what a model has learned? What are the limitations of probing classifiers, and how can they be addressed? Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. Gain familiarity with the PyTorch and HuggingFace libraries, for using and evaluating

Explore this link on the map →

related reading