6 citations · 6 across the 8 of their papers we have counts for
12 papers
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs
Gonzalo Mancera, Julian Fierrez, Aythami Morales +3
This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine whether specific samples we…
CrimeNER Demo: Named-Entity Recognition in the Crime Domain
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
We present CrimeNER Demo, an AI-powered platform that enables us to extract general crime-related information from documents and classify them into entity types with two levels of…
Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2
Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez +3
We present the Membership Inference Test (MINT) Demo 2, a framework designed to improve transparency in machine learning training processes. MINT is a technique for experimentally…
Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +3
We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP…
CrimeNER: Curated Dataset and Methods for Research and Investigation in Language Intelligence
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named…
Membership Inference Test: Auditing Training Data in Object Classification Models
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +2
In this research, we analyze the performance of Membership Inference Tests (MINT), focusing on determining whether given data were utilized during the training phase, specifically…
Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning
Daniel DeAlcala, Aythami Morales, Julian Fierrez +3
Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a no…
PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)
Gonzalo Mancera, Aythami Morales, Julian Fierrez +5
The use of Natural Language Processing (NLP) in highstakes AI-based applications has increased significantly in recent years, especially since the emergence of Large Language Model…
Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment
Alejandro Peña, Julian Fierrez, Aythami Morales +3
The use of language technologies in high-stake settings is increasing in recent years, mostly motivated by the success of Large Language Models (LLMs). However, despite the great p…
Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs
Gonzalo Mancera, Daniel DeAlcala, Julian Fierrez +2
This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if g…
MINT-Demo: Membership Inference Test Demonstrator
Daniel DeAlcala, Aythami Morales, Julian Fierrez +3
We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determ…
Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics
Daniel DeAlcala, Aythami Morales, Julian Fierrez +3
This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifica…