Large language models

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.

Large language models alternate multilayer perceptrons and self-attention to process token sequences. Gabriel Peyré compares the proof methods and approximation capabilities of these components. Whereas perceptron approximation is well understood, attention presents less explored mathematical questions. The talk connects their expressive power to approximation theory in infinite-dimensional spaces representing distributions of input tokens and identifies open problems at that interface.

…and artificial intelligence, including large language models. The ideal candidate runs collaborative dry-lab research aligned with departmental strengths such as learning and memory, decision making, motor control, affective neuro

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 audio edition of Nature’s feature examines concerns that widespread reliance on large language models could weaken critical thinking. It draws on research in cognition and education to consider how people can preserve and practise reasoning skills while using AI tools. The feature distinguishes the concern about cognitive offloading from evidence and practical approaches developed by researchers studying critical thought.

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

A major goal of neuroscience is to discover mathematical models that describe how the brain implements cognitive processes like learning and decision-making. Historically, the field has relied on handcrafted models, often motivated by normative considerations about optimal choice behavior, which are then modified to fit the idiosyncrasies of animal behavior. These models re-quire substantial effort to devise, and are often limited in their ability to fully predict animal behavior. However, these programmatic models provide insight and afford interpretation: variables like“prediction err

…to mirative markers in humans and large language models (

…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

…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

…behavior), training machine learning models (DeepLabCut, CEBRA etc.), writing post-hoc analysis code, and/or running visualization tools. Those pre-defined sub-tasks require domain expertise in coding, machine learning, and often heavy manual annotation. Yet, there has not been one system that can help abstract away all the sub-tasks and focus on interaction-based programming to meet researchers’ high-level language queries. Here, inspired by the design of Software-3.0, we introduce a

The success of Large Language Models (LLM) has led to controversial hypotheses about emergent intelligence beyond language competence, amongst which Theory of Mind (ToM) has become the most debated. While some reported gpt-4 to pass the Sally-Anne test, others reported limitations. Here we aim to validate LLM ability to recognise ToM occurrences in natural human dialogue using the activity of ToM-associated brain regions.The current analysis uses an existing corpus of 25 participants recorded with functional MRI while carrying conversations in 4 sessions x 6 trials alternating a Human and a Ro

…Machine Learning (ML) & Natural Language Processing (NLP) to effectively manage and organize a growing collection of scientific content.Specifically, we focus on World-Wide.org, a platform hosting >3,000 scientific seminars. We use a fine-tuned DeBERTa langua

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

…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

Andrea Martin develops an account of language that combines compositional structure with physiological constraints from human brains. She discusses neural dynamics and low-dimensional manifolds as a way to connect statistical regularities with formal organization, and considers how this approach relates to the capabilities and limitations of large language models.

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