Report
From Words to Worlds: Compositionality for Cognitive Architectures
| العنوان: | From Words to Worlds: Compositionality for Cognitive Architectures |
|---|---|
| المؤلفون: | Dhar, Ruchira, Søgaard, Anders |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Symbolic Computation |
| الوصف: | Large language models (LLMs) are very performant connectionist systems, but do they exhibit more compositionality? More importantly, is that part of why they perform so well? We present empirical analyses across four LLM families (12 models) and three task categories, including a novel task introduced below. Our findings reveal a nuanced relationship in learning of compositional strategies by LLMs -- while scaling enhances compositional abilities, instruction tuning often has a reverse effect. Such disparity brings forth some open issues regarding the development and improvement of large language models in alignment with human cognitive capacities. Comment: Accepted to ICML 2024 Workshop on LLMs & Cognition |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.13419 |
| رقم الانضمام: | edsarx.2407.13419 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |