<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository provides comprehensive course materials for the graduate-level "Interpretability of Large Language Models (0368.4264)" at Tel Aviv University. Designed for students with prior NLP and ML backgrounds, it offers a collaborative, active-learning environment focused on understanding LLM interpretability through weekly paper readings, discussions, and hands-on coding exercises, serving as a valuable resource for researchers and practitioners.

How It Works

The course adopts an immersive, active-learning methodology, structured as a collaborative research group. Participants engage with seminal and contemporary research papers weekly, fostering in-depth understanding through guided discussions, role-playing, and practical, hands-on coding exercises. This approach aims to equip students with the critical thinking and practical skills necessary to navigate and contribute to the LLM interpretability field.

Quick Start & Requirements

This repository serves as educational course material rather than a deployable software project. Key prerequisites include a solid background in natural language processing and machine learning. While specific installation instructions are absent, the presence of "Coding exercises and challenges" with accompanying "Exercise Solution" files indicates practical implementation is integral to the course.

Highlighted Details

• Core Topics: Covers fundamental LLM interpretability techniques including probing, representation inspection, analysis of attention heads and MLP layers, neuron-level analysis, feature representation, and circuit discovery.
• Advanced Concepts: Delves into sophisticated areas such as binding mechanisms, factual knowledge recall and editing within models, and the dynamics of LLM training.
• Pedagogical Approach: Structured around weekly deep dives into influential research papers, complemented by discussions and role-playing, creating a research-oriented learning experience.
• Practical Application: Features hands-on coding exercises and challenges designed to reinforce theoretical concepts and develop practical skills in analyzing LLM internals.

Maintenance & Community

The course materials were developed by Dr. Mor Geva and Daniela Gottesman at Tel Aviv University, with acknowledgments for contributions from Amit Elhelo, Or Shafran, and Yoav Gur-Arieh. For inquiries or suggestions, the recommended channel is to open an issue within the repository.

Licensing & Compatibility

The provided README does not specify a software license, nor does it detail compatibility for commercial use or closed-source linking.

Limitations & Caveats

This repository contains educational materials and exercises, not a ready-to-use tool. The course schedule is noted as subject to minor changes. A significant prerequisite is a strong existing foundation in NLP and machine learning, which may present a barrier for individuals new to these domains.