Telecomunicaciones, información y comunicación

A Systematic Comparison between Extractive Self-Explanations and Human Rationales in Text Classification

- Instruction-tuned LLMs are able to provide \textit{an} explanation about their output to users by generating self-explanations, without requiring the application of complex interpretability...

Toxic Subword Pruning for Dialogue Response Generation on Large Language Models

- How to defend large language models (LLMs) from generating toxic content is an important research area. Yet, most research focused on various model training techniques to remediate LLMs by updating...

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees

- Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection, particularly in extractive question answering. This challenge is magnified in...

Testing Support Size More Efficiently Than Learning Histograms

- Consider two problems about an unknown probability distribution $p$:1. How many samples from $p$ are required to test if $p$ is supported on $n$ elements or not? Specifically, given samples from...

Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models

- There are two shortages in the current Large Language Models (LLMs) era. The first is short of multilingual models, where most LLMs are English-centric and performance is limited on multilingual...

Fast Byzantine Total Order Broadcast

- This paper presents Flutter, the first Byzantine Total Order Broadcast implementation with a broadcast-to-delivery latency of $2\Delta + \epsilon$ time units, $\Delta$ being the message delay and...

Protecting Cryptographic Libraries against Side-Channel and Code-Reuse Attacks

- Cryptographic libraries, an essential part of cybersecurity, are shown to be susceptible to different types of attacks, including side-channel and memory-corruption attacks. In this article, we...

Towards the Anonymization of the Language Modeling

- Rapid advances in Natural Language Processing (NLP) have revolutionized many fields, including healthcare. However, these advances raise significant privacy concerns, especially when pre-trained...

Optimizing Navigational Graph Queries

- We study the optimization of navigational graph queries, i.e., queries which combine recursive and pattern-matching fragments. Current approaches to their evaluation are not effective in practice....

TRAM: Test-Time Risk Adaptation with Mixture of Agents

- Deployed reinforcement learning agents often face safety requirements that are specified only after training, such as new hazard maps, revised risk thresholds, or behavioral alignment constraints. We...

Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence

- Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial...

Large-Step Training Dynamics of a Two-Factor Linear Transformer Model

- Gradient-flow analyses show that simplified linear transformers can learn the in-context linear-regression algorithm, but they do not explain the finite-step behavior of gradient descent at large...

Bitcoin's Power Law: Weak Structure, Strong Forecasts

- Bitcoin's price has been described as following a power law (PL) in time, $P \sim t^{\beta}$ with $\hat\beta \approx 5.7$ over 2010-2026. We test this claim using the Clauset-Shalizi-Newman...

Stimulus symmetries can confound representational similarity analyses

- What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the need for a more complete characterization of...

Beyond Nonlinear Small-Gain Design: DADS with Partial-State Feedback

- Eduardo Sontag and coauthors studied Input-to-Output Stability (IOS) and the output asymptotic gain property. These notions changed control theory and recently had an impact on robust adaptive...

Memorisation, convergence and generalisation in generative models

- Generative neural networks learn how to produce highly realistic images from a large, but finite number of examples - or do they simply memorise their training set? To settle this question,...

Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment

- Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is...

Automatically Learning Construction Injury Precursors from Text

- In light of the increasing availability of digitally recorded safety reports in the construction industry, it is important to develop methods to exploit these data to improve our understanding of...

AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes

- This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted from attributes with machine learning. Like in the...

The Score-Difference Flow for Implicit Generative Modeling

- Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work (e.g. score-matching networks, diffusion models)...

Mercer Large-Scale Kernel Machines from Ridge Function Perspective

- To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from {\it Fundamentality of ridge functions}. We consider the main result of...