We are a collocation of collaborators working on a diverse range of topics in computational linguistics, natural language processing and machine learning.
Credits to Afra for the lab logo and to Tim for the logo idea.
*Alumna is the nominative plural of alumnum, which means student in Latin. This a gender neutral (or, better said, gender neuter) version of the gendered words alumni and alumnae.
ETH Zürich Spring 2025 This course explores the connection between automata and formal logic. More precisely, it covers the algebraic characterization of the regular languages definable in many different logical theories, the complexity theory of boolean circuits, and the connection between the two.
ETH Zürich Spring 2025 This graduate class, partly taught like a seminar, is designed to help you understand the philosophical underpinnings of modern work in natural language processing (NLP), most of which is centered around statistical machine learning applied to natural language data.
ETH Zürich Spring 2025 In the first part of the seminar, we study some of the most popular parsing algorithms, which are a fundamental tool both in natural language processing and in programming languages. Each week, a student will present a paper on parsing, including the papers that first described celebrated parsing algorithms like Earley’s and CKY. We will also put a lot of focus on weighted parsing, which is fundamental in applications to language modeling. In the second part, we’ll examine advanced NLP topics through analysis of pivotal (and often controversial) papers that are shaping the field’s future direction.
ETH Zürich Fall 2024 This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
ETH Zürich Fall 2024 This Bachelor’s seminar delves into the fascinating world of modern large language models (LLMs), which have revolutionized natural language processing. As these models continue to evolve and impact various domains, we will explore their potential, limitations, and underlying mechanisms through a theroretical lens. Throughout the seminar, we will address the following key questions: what are the real capabilities of large language models? What are their inherent limitations? How do these models function at a fundamental level? Under what circumstances are they likely to fail? Can we develop a comprehensive “science of LLMs” to address these inquiries? We will leverage formal language theory to provide a rigorous framework for understanding the representational capacity of neural language models.
ETH Zürich Spring 2024 In recent years, NLP has become a part of our daily lives. Many of us use tools like Google Translate to understand sentences in languages we don’t know, and chatbots like ChatGPT to help draft essays and answer basic questions. However, even though most people recognize the utility of such tools, there are still many questions to be answered about their reliability and their impact on society. For example, to what extent can we or should we trust what ChatGPT says? Should chatbots ever be used in legal decision-making? What is the role that NLP should play in the education system? In this open-ended seminar, we will read and discuss opinions on the proper use of NLP in the real world, or as we term it, NLP in the wild!
ETH Zürich Spring 2025 Large language models have become one of the most commonly deployed NLP inventions. In the past half-decade, their integration into core natural language processing tools has dramatically increased the performance of such tools, and they have entered the public discourse surrounding artificial intelligence. In this course, we start with the probabilistic foundations of language models, i.e., covering what constitutes a language model from a formal, theoretical perspective. We then discuss how to construct and curate training corpora, and introduce many of the neural-network architectures often used to instantiate language models at scale. The course discusses privacy and harms, as well as applications of language models in NLP and beyond.
ETH Zürich Spring 2024 This graduate class, partly taught like a seminar, is designed to help you understand the philosophical underpinnings of modern work in natural language processing (NLP), most of which centered around statistical machine learning applied to natural language data.
ACL (Toronto) July 2023 In this tutorial, we will provide a centralized and cohesive discussion of critical considerations when choosing how to generate text from a language model. We will cover a wide range of empirically-observed problems (like degradation, hallucination, repetition) and their corresponding proposed algorithmic solutions from recent research (like top-p sampling and its successors). We will then cover methods in controlled generation, that go beyond just ensuring coherence to ensure text exhibits specific desired properties.
ESSLLI (Ljubljana, Slovenia) Spring 2023
If you are a BSc or MSc student at ETH Zurich interested in writing your thesis with us, we would be delighted to hear from you! Unfortunately, we do not have the capacity to consider students from outside ETH for thesis projects. To obtain a better understanding of what currently interests us, we invite you to check our most recent publications. However, feel free to express interest in any topic you think our group might be well suited to advise you on.
Specifcally for Bachelor theses or semester projects, we typically assign you one of our published papers to replicate, so it would be ideal if you indicate 3-4 of our publications that you are interested in.
Please send an email to ryan.cotterell@inf.ethz.ch with CC to afra.amini@inf.ethz.ch, anej.svete@inf.ethz.ch, and eleftheria.tsipidi@inf.ethz.ch. State either [bachelor’s thesis] or [master’s thesis] at the start of the subject. For us to get to know you a little, please write a paragraph introducing your interests and attach your CV as well as your transcript of grades. It helps us a lot to with finding a matching project if you are able to state more concrete topics that you would like to work on. We are looking forward to receiving your inquiry!
Thank you very much for your interest in joining our group – unfortunately, we are not accepting PhD students anymore!
If you are interested in working with us as a Master’s student, please see here. Ryan has previously co-advised Master’s students on NLP topics with Mrinmaya Sachan and others, if co-advising is an option you would like to pursue.
