26838 research outputs found
Sort by
Celebrating women in extended reality: Insights and inspiration from the women in XR webinar
No full textDesigning the space archivists: A metadata-driven VR game concept for children to engage with cultural heritage
No full textTransformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles with Minimal Data Across Thousands of Domains
No full textThis is the official repository for the paper An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of Domains
Navigating the political compass: Evaluating multilingual LLMs across languages and nationalities
No full textLarge Language Models (LLMs) have become ubiquitous in today’s technological landscape, boasting a plethora of applications and even endangering human jobs in complex and creative fields. One such field is journalism: LLMs are being used for summarization, generation, and even fact-checking. However, in today’s political landscape, LLMs could accentuate tensions if they exhibit political bias. In this work, we evaluate the political bias of the 15 most-used multilingual LLMs via the Political Compass Test. We test different scenarios, where we vary the language of the prompt while also assigning a nationality to the model. We evaluate models on the 50 most populous countries and their official languages. Our results indicate that language has a strong influence on the political ideology displayed by a model. In addition, smaller models tend to display a more stable political ideology, i.e. ideology that is less affected by variations in the prompt
LLM4Eval: Large Language Model for Evaluation in IR
No full textLarge language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. Building on the success of previous workshops, which established foundations in automated judgments and RAG evaluation, this third iteration aims to address emerging challenges as IR systems become increasingly personalized and interactive. The main goal of the third LLM4Eval workshop is to bring together researchers from industry and academia to explore three critical areas: the evaluation of personalized IR systems while maintaining fairness, the boundaries between automated and human assessment in subjective scenarios, and evaluation methodologies for systems that combine multiple IR paradigms (search, recommendations, and dialogue). By examining these challenges, we seek to understand how evaluation approaches can evolve to match the sophistication of modern IR applications. The format of the workshop is interactive, including roundtable discussion sessions, fostering dialogue about the future of IR evaluation while avoiding one-sided discussions. This is the third iteration of the workshop series, following successful events at SIGIR 2024 and WSDM 2025, with the first iteration attracting over 50 participants
Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation
Get PDFWe propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm “discretize first, filter and close next”. This leads to full model-data consistency and allows for employing neural closure models in the same environment as where they have been trained. Since the LES discretization error is included in the learning process, the closure models can learn to account for the discretization. Furthermore, we employ a divergence-consistent discrete filter defined through face-averaging and provide novel theoretical and numerical filter analysis. This filter preserves the discrete divergence-free constraint by construction, unlike general discrete filters such as volume-averaging filters. We show that using a divergence-consistent LES formulation coupled with a convolutional neural closure model produces stable and accurate results for both a-priori and a-posteriori training, while a general (divergence-inconsistent) LES model requires a-posteriori training or other stability-enforcing measures
A non-homogeneous alternating renewal process model for interval censoring
Get PDFPrevious approaches to modelling interval-censored data have often relied on assumptions of homogeneity in the sense that the censoring mechanism, the underlying distribution of occurrence times, or both, are assumed to be time-invariant. In this work, we introduce a model which allows for non-homogeneous behaviour in both cases. In particular, we outline a censoring mechanism based on a non-homogeneous alternating renewal process in which interval generation is assumed to be time-dependent, and we propose a Markov point process model for the underlying occurrence time distribution. We prove the existence of this process and derive the conditional distribution of the occurrence times given the intervals. We provide a framework within which the process can be accurately modelled, and subsequently compare our model to the homogeneous approach through a number of illustrative examples
On the correlation gap of matroids
Get PDFA set function can be extended to the unit cube in various ways; the correlation gap measures the ratio between two natural extensions. This quantity has been identified as the performance guarantee in a range of approximation algorithms and mechanism design settings. It is known that the correlation gap of a monotone submodular function is at least 1-1/e, and this is tight for simple matroid rank functions. We initiate a fine-grained study of the correlation gap of matroid rank functions. In particular, we present an improved lower bound on the correlation gap as parametrized by the rank and girth of the matroid. We also show that for any matroid, the correlation gap of its weighted rank function is minimized under uniform weights. Such improved lower bounds have direct applications for submodular maximization under matroid constraints, mechanism design, and contention resolution schemes
To be greedy, or not to be — That is the question for Population Based Training variants
Get PDFAchieving excellent results with neural networks requires careful hyperparameter tuning, which can be automated via hyperparameter optimization algorithms such as Population Based Training (PBT). PBT stands out for its capability to efficiently optimize hyperparameter schedules in parallel and within the wall-clock time of training a single network. Several PBT variants have been proposed that improve performance in the experimental settings considered in the associated publications. However, the experimental settings and tasks vary across publications, while the best previous PBT variant is not always included in the comparisons, thus making the relative performance of PBT variants unclear. In this work, we empirically evaluate five single-objective PBT variants on a set of image classification and reinforcement learning tasks with different setups (such as increasingly large search spaces). We find that the Bayesian Optimization (BO) variants of PBT tend to behave greedier than the non-BO ones, which is beneficial when aggressively pursuing short-term gains improves long-term performance and harmful otherwise. This is a previously overlooked caveat to the reported improvements of the BO PBT variants. Examining their theoretical properties, we find that the returns of BO PBT variants are guaranteed to asymptotically approach the returns of the greedy hyperparameter schedule (rather than the optimal one, as claimed in prior work). Together with our empirical results, this leads us to conclude that there is currently no single best PBT variant capable of outperforming others both when pursuing short-term gains is helpful in the long term, and when it is harmful