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As LLMs grow and ai applications become more powerful, the quest to better understand their inner workings becomes more difficult and more acute. Conversations about the risks of black box models aren't exactly new, but as the footprint of ai-powered tools continues to grow and hallucinations and other suboptimal outcomes arrive in browsers and user interfaces with alarming frequency More importantly, it is always necessary for professionals (and end users) to resist the temptation to accept ai-generated content at face value.
Our selection of weekly highlights delves into the issue of model interpretability and explainability in the era of widespread LLM use. From detailed analyzes of an influential new paper to hands-on experiments with other recent techniques, we hope you'll take some time to explore this ever-crucial topic.
- Manually Dive into Anthropic Sparse Autoencoders
In just a few weeks, the Anthropic paper “Scaling Monosemanticity” has attracted a lot of attention within the XAI community. Srijanie Dey, PhD presents a beginner's manual for anyone interested in the researchers' claims and goals, and how they came up with an “innovative approach to understanding how the different components of a neural network interact with each other and what role each component plays.” - Interpretable functions in large language models
For a high-level, well-illustrated explanation of the theoretical underpinnings of the “Scaling Monosemanticity” article, we highly recommend Jeremy Nuerfrom the TDS debut article – will leave you with a firm understanding of the researchers' thinking and what is at stake in this work for future model development: “as improvements plateau and it becomes more difficult to scale LLMs , it will be important to truly understand how they work if we want to make the next leap in performance.” - The meaning of explainability for ai
Taking a few steps back from specific models and the technical challenges they create in their wake, Stephanie Kirmer gets “a little philosophical” in her article about the limits of interpretability; Attempts to illuminate such black-box models may never achieve full transparency, she argues, but they are still important for ML researchers and developers to invest in.
- Additive decision trees
In his recent work, Brett Kennedy has focused on interpretable predictive models, analyzing their underlying mathematics and showing how they work in practice. His recent deep dive into additive decision trees is a powerful and comprehensive introduction to such a model, showing how it is intended to complement the limited options available for interpretable classification and regression models. - Deep Dive into Cumulative Local Effects (ALE) Plots with Python
To complete our selection, we are happy to share Conor O'SullivanPractical Exploration of Cumulative Local Effects (ALE) Plots – An older but reliable method to provide clear interpretations even in the presence of multicollinearity in your model.
