30 citations · 94 across the 30 of their papers we have counts for
8 papers · 1 filter
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…