3 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
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.…
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…
Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal
Gonzalo Martínez, Juan Diego Molero, Sandra González +3
This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence and arousal for multi-word expressions. Unlike previous…
Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language Models
Gonzalo Martínez, Javier Conde, Elena Merino-Gómez +4
Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama, Mistral, and…