The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, its complexity makes it difficult for non-experts to learn. Existing resources often lack interactivity, rely on static descriptions of simplified architectures, or fail to reflect models’ behavior with real data. To address this gap, we introduce Transformer Explainer, an interactive visualization tool for non-experts to learn Transformers. The tool integrates an overview illustrating the Transformer’s data flow with on-demand explanations that gradually reveal mathematical details. Smooth transitions across abstraction levels highlight the interplay between high-level structures and low-level operations. Running a live GPT-2 instance directly in the browser, Transformer Explainer empowers learners to experiment with custom input and hyperparameters without setup, observing next-token predictions in real time. A 90-participant user study showed that our tool offered significant advantages in improving user understanding and engagement. Transformer Explainer has attracted over 490,000 users.

Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present TRANSFORMER EXPLAINER, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by integrating a model overview and smooth transitions across abstraction levels of math operations and model structures. It runs a live GPT-2 model locally in the user’s browser, empowering users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. 125,000 users have used our open-source tool at https://poloclub.github.io/ transformer-explainer/.

As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical.Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys.We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them.Our novel taxonomy, organized by LLM workflow stages, summarizes nearly 70 works at their intersections.We conclude with open challenges and future directions.This timely survey helps researchers and practitioners navigate key advancements for safer, more interpretable LLMs.

Multi-modal models, such as CLIP, have demonstrated strong performance in aligning visual and textual representations, excelling in tasks like image retrieval and zero-shot classification. Despite this success, the mechanisms by which these models utilize training data, particularly the role of memorization, remain unclear. In uni-modal models, both supervised and self-supervised, memorization has been shown to be essential for generalization. However, it is not well understood how these findings would apply to CLIP, which incorporates elements from both supervised learning via captions that provide a supervisory signal similar to labels, and from self-supervised learning via the contrastive objective. To bridge this gap in understanding, we propose a formal definition of memorization in CLIP (CLIPMem) and use it to quantify memorization in CLIP models. Our results indicate that CLIP’s memorization behavior falls between the supervised and self-supervised paradigms, with "mis-captioned" samples exhibiting highest levels of memorization. Additionally, we find that the text encoder contributes more to memorization than the image encoder, suggesting that mitigation strategies should focus on the text domain. Building on these insights, we propose multiple strategies to reduce memorization while at the same time improving utility---something that had not been shown before for traditional learning paradigms where reducing memorization typically results in utility decrease.

It's no secret that AI systems come with a significant environmental cost. This raises the question: What are the roles and responsibilities of computing professionals regarding the sustainability of AI? Informed by a year-long informal literature review on the subject, we employ stakeholder identification, analysis, and mapping to highlight the complex and interconnected roles that five major stakeholder groups (industry, practitioners, regulatory, advocacy, and the general public) play in the sustainability of AI. Swapping the traditional final step of stakeholder methods (stakeholder engagement) for entanglement, we demonstrate the inherent entwinement of choices made with regard to the development and maintenance of AI systems and the people who impact (or are impacted by) these choices. This entanglement should be understood as a system of human and non-human agents, with the implications of each choice ricocheting into the use of natural resources and climate implications. We argue that computing professionals (AI-focused or not) may belong to multiple stakeholder groups, and that we all have multiple roles to play in the sustainability of AI. Further, we argue that the nature of regulation in this domain will look unlike others in environmental preservation (e.g., legislation around water contaminants). As a result, we call for ongoing, flexible bodies and policies to move towards the regulation of AI from a sustainability angle, as well as suggest ways in which individual computing professionals can contribute to fighting the environmental and climate effects of AI.