Topic: large language models

Jin Zhu examines statistical approaches to identifying text produced by large language models. The increasing availability of fluent generated writing raises questions about misinformation, academic integrity and the authenticity of digital content. Although detection has attracted substantial machine-learning research, the talk argues that its statistical foundations remain underdeveloped. Zhu presents recent work on principled, computationally efficient detection methods, including approaches reported at NeurIPS and ICLR. An interactive demonstration accompanies the research, illustrating on

The growth of machine-learning conference submissions and rapid uptake of large language models have accelerated research, while leaving open the question of whether findings have become more reliable. A favourable random seed or contamination of a test set can be mistaken for a scientific advance. This lecture examines the resulting gap in trust, analyses common research practices that produce fragile conclusions, and discusses safeguards needed for machine-learning studies to make a durable scientific contribution.

Develop a framework for network tomography combining graph neural networks with large language models. The GENIE ANR project investigates generalization across unseen network topologies, inference of non-additive metrics, and Graph-RAG diagnostics grounded in incident records and routing information. The doctorate includes experimental validation on realistic network data and peer-reviewed dissemination, within the ERMINE team in collaboration with ADOPNET. Appointment: 3 years; advertised start 2027-01-04. monthly gross salary 2300 euros.

Investigate generative AI for engineering design, combining large language models with formal design grammars. Explore how models represent, reason about and generate designs with human collaborators, with applications in products, manufacturing and structures. The fully funded salaried doctorate includes teaching support in design, product development and CAD, and begins between February and September 2027. Apply through the ETH online recruitment portal. Application deadline: 2026-11-01.

Develop data and algorithmic solutions to advance safety and fairness in Gemini models, including post-training of large language models, adversarial robustness improvement, and evaluation protocol design.

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech

Generative AI is fundamentally transforming the software development industry by improving processes such as software testing, bug detection, bug fixes, and developer productivity. This talk explores how AI-driven techniques, particularly large language models (LLMs), are being utilized to generate realistic test scenarios, automate bug detection and repair, and streamline development workflows. As these technologies evolve, they promise to improve software quality and efficiency significantly. The discussion will cover key methodologies, challenges, and the future impact of generative AI on t

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the ad

Qualitative coding is the process of categorizing and labeling raw data to identify themes, patterns, and concepts within qualitative research. This process requires significant time, reflection, and discussion, often characterized by inherent subjectivity and uncertainty. Here, we explore the possibility to leverage large language models (LLM) to enhance the process and assist researchers with qualitative coding. LLMs, trained on extensive human-generated text, possess an architecture that renders them capable of understanding the broader context of a conversation or text. This allows them to

Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s hom

Mirative markers, such as “surprisingly”, are linguistic elements that encode a violation of expectations (Delancey, 1997, 2012; Aikhenvald, 2012). Such markers are used for expectation management during communication, informing the listener about an upcoming unexpected message. Sensitivity to mirative markers relies on two abilities: i) updating expectations upon recognizing a mirative marker, and ii) identifying expectation violations warranting the use of a mirative marker. The aim of this study is to quantify and compare sensitivity to mirative markers in humans and large language models (