30 citations · 94 across the 30 of their papers we have counts for
33 papers
Decoding-Level Taboo: A Diagnostic Stress Test for LLM Robustness
Tadanobu Chuyo Kamijo, Ori Rottenstreich, Javier Conde +2
Large language model evaluations typically focus on performance under nominal conditions, creating an illusion of capability where models comfortably walk a narrow, highly optimize…
Beyond the Hivemind: Escaping LLM Homogeneity via Meta-Persona Anchoring and Sequential Temperature Scaling
Tairan Fu, Javier Conde, Carlos Arriaga +3
Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized consensus even for open questio…
Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
Samer Awad, Javier Conde, Carlos Arriaga +3
Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. While previous research has foc…
Lost in Motion: Vision Language Models Fail the Dynamic Gauges Test
Tairan Fu, Francisco Javier Santos-Martín, Javier Conde +2
The digital transformation of industrial manufacturing increasingly relies on the ability of autonomous robots to interact with legacy infrastructure, particularly analog gauges. V…
To Words and Beyond: Probing Large Language Models for Sentence-Level Psycholinguistic Norms of Memorability and Reading Times
Thomas Hikaru Clark, Carlos Arriaga, Javier Conde +2
Large Language Models (LLMs) have recently been shown to produce estimates of psycholinguistic norms, such as valence, arousal, or concreteness, for words and multiword expressions…
Large Language Models and Book Summarization: Reading or Remembering, Which Is Better?
Tairan Fu, Javier Conde, Pedro Reviriego +3
Summarization is a core task in Natural Language Processing (NLP). Recent advances in Large Language Models (LLMs) and the introduction of large context windows reaching millions o…
How does fine-tuning improve sensorimotor representations in large language models?
Minghua Wu, Javier Conde, Pedro Reviriego +1
Large Language Models (LLMs) exhibit a significant "embodiment gap", where their text-based representations fail to align with human sensorimotor experiences. This study systematic…
Beyond Reproducibility: Token Probabilities Expose Large Language Model Nondeterminism
Tairan Fu, Gonzalo Martínez, Javier Conde +4
The execution of Large Language Models (LLMs) has been shown to produce nondeterministic results when run on Graphics Processing Units (GPUs), even when they are configured to prod…
Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings
Javier Conde, María Grandury, Tairan Fu +7
Word-level psycholinguistic norms lend empirical support to theories of language processing. However, obtaining such human-based measures is not always feasible or straightforward.…
Stochastic Streets: A Walk Through Random LLM Address Generation in four European Cities
Tairan Fu, David Campo-Nazareno, Javier Coronado-Blázquez +3
Large Language Models (LLMs) are capable of solving complex math problems or answer difficult questions on almost any topic, but can they generate random street addresses for Europ…
Artificial Intelligence and Misinformation in Art: Can Vision Language Models Judge the Hand or the Machine Behind the Canvas?
Tarian Fu, Javier Conde, Gonzalo Martínez +3
The attribution of artworks in general and of paintings in particular has always been an issue in art. The advent of powerful artificial intelligence models that can generate and a…
The Generative Energy Arena (GEA): Incorporating Energy Awareness in Large Language Model (LLM) Human Evaluations
Carlos Arriaga, Gonzalo Martínez, Eneko Sendin +2
The evaluation of large language models is a complex task, in which several approaches have been proposed. The most common is the use of automated benchmarks in which LLMs have to…
La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America
María Grandury, Javier Aula-Blasco, Júlia Falcão +22
Leaderboards showcase the current capabilities and limitations of Large Language Models (LLMs). To motivate the development of LLMs that represent the linguistic and cultural diver…
Is There a Case for Conversation Optimized Tokenizers in Large Language Models?
Raquel Ferrando, Javier Conde, Gonzalo Martínez +1
The computational and energy costs of Large Language Models (LLMs) have increased exponentially driven by the growing model sizes and the massive adoption of LLMs by hundreds of mi…
Psycholinguistic Word Features: a New Approach for the Evaluation of LLMs Alignment with Humans
Javier Conde, Miguel González, María Grandury +3
The evaluation of LLMs has so far focused primarily on how well they can perform different tasks such as reasoning, question-answering, paraphrasing, or translating. For most of th…
Have Multimodal Large Language Models (MLLMs) Really Learned to Tell the Time on Analog Clocks?
Tairan Fu, Miguel González, Javier Conde +2
Multimodal Large Language Models which can answer complex questions on an image struggle to tell the time on analog clocks. This is probably due to the lack of images with clocks a…
Real-time Spatial Retrieval Augmented Generation for Urban Environments
David Nazareno Campo, Javier Conde, Álvaro Alonso +3
The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundatio…
Speed and Conversational Large Language Models: Not All Is About Tokens per Second
Javier Conde, Miguel González, Pedro Reviriego +3
The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the mo…
Understanding the Impact of Artificial Intelligence in Academic Writing: Metadata to the Rescue
Javier Conde, Pedro Reviriego, Joaquín Salvachúa +3
This column advocates for including artificial intelligence (AI)-specific metadata on those academic papers that are written with the help of AI in an attempt to analyze the use of…
Can ChatGPT Learn to Count Letters?
Javier Conde, Gonzalo Martínez, Pedro Reviriego +3
Large language models (LLMs) struggle on simple tasks such as counting the number of occurrences of a letter in a word. In this paper, we investigate if ChatGPT can learn to count…
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong
Tairan Fu, Javier Conde, Gonzalo Martínez +2
Multiple Choice Question (MCQ) tests are among the most used methods for evaluating large language models (LLMs). Besides checking the correctness of the selected answer, evaluatio…
Why Do Large Language Models (LLMs) Struggle to Count Letters?
Tairan Fu, Raquel Ferrando, Javier Conde +2
Large Language Models (LLMs) have achieved unprecedented performance on many complex tasks, being able, for example, to answer questions on almost any topic. However, they struggle…
Analyzing Recursiveness in Multimodal Generative Artificial Intelligence: Stability or Divergence?
Javier Conde, Tobias Cheung, Gonzalo Martínez +2
One of the latest trends in generative Artificial Intelligence is tools that generate and analyze content in different modalities, such as text and images, and convert information…
Evaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?
Marina Mayor-Rocher, Nina Melero, Elena Merino-Gómez +3
Large Language Models (LLMs) have been profusely evaluated on their ability to answer questions on many topics and their performance on different natural language understanding tas…