A fusion of vision DNN and LLM representations predicts human EEG responses to images better than either model alone, with distinct temporal and spectral signatures.
The dynamics of meaning through time: Assessment of Large Language Models
1 Pith paper cite this work, alongside 3 external citations. Polarity classification is still indexing.
1
Pith paper citing it
3
external citations · Pith
abstract
Understanding how large language models (LLMs) grasp the historical context of concepts and their semantic evolution is essential in advancing artificial intelligence and linguistic studies. This study aims to evaluate the capabilities of various LLMs in capturing temporal dynamics of meaning, specifically how they interpret terms across different time periods. We analyze a diverse set of terms from multiple domains, using tailored prompts and measuring responses through both objective metrics (e.g., perplexity and word count) and subjective human expert evaluations. Our comparative analysis includes prominent models like ChatGPT, GPT-4, Claude, Bard, Gemini, and Llama. Findings reveal marked differences in each model's handling of historical context and semantic shifts, highlighting both strengths and limitations in temporal semantic understanding. These insights offer a foundation for refining LLMs to better address the evolving nature of language, with implications for historical text analysis, AI design, and applications in digital humanities.
fields
q-bio.NC 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
The time course of visuo-semantic representations in the human brain is captured by combining vision and language models
A fusion of vision DNN and LLM representations predicts human EEG responses to images better than either model alone, with distinct temporal and spectral signatures.