Evaluating Adjective-Noun Compositionality in LLMs: Functional vs Representational Perspectives
arXiv:2603.09994v1 Announce Type: cross Abstract: Compositionality is considered central to language abilities. As performant language systems, how do large language models (LLMs) do on compositional tasks? We evaluate adjective-noun compositionality in LLMs using two complementary setups: prompt-based functional assessment and a representational analysis of internal model states. Our results reveal a striking divergence between task performance and internal states. While LLMs reliably develop compositional representations, they fail to translate consistently into functional task success across model variants. Consequently, we highlight the importance of contrastive evaluation for obtaining a more complete understanding of model capabilities.
arXiv:2603.09994v1 Announce Type: cross Abstract: Compositionality is considered central to language abilities. As performant language systems, how do large language models (LLMs) do on compositional tasks? We evaluate adjective-noun compositionality in LLMs using two complementary setups: prompt-based functional assessment and a representational analysis of internal model states. Our results reveal a striking divergence between task performance and internal states. While LLMs reliably develop compositional representations, they fail to translate consistently into functional task success across model variants. Consequently, we highlight the importance of contrastive evaluation for obtaining a more complete understanding of model capabilities.
Executive Summary
The article Evaluating Adjective-Noun Compositionality in LLMs: Functional vs Representational Perspectives examines the compositional abilities of large language models (LLMs) through two assessment methods: functional and representational analysis. The findings indicate a disparity between the models' internal compositional representations and their functional performance on tasks, highlighting the need for a comprehensive evaluation approach to understand LLM capabilities fully.
Key Points
- ▸ Compositionality is a central aspect of language abilities
- ▸ LLMs develop compositional representations but struggle with consistent functional task success
- ▸ Contrastive evaluation is crucial for a complete understanding of model capabilities
Merits
Comprehensive Methodology
The use of both prompt-based functional assessment and representational analysis provides a thorough understanding of LLMs' compositional abilities
Demerits
Limited Generalizability
The study's focus on adjective-noun compositionality might limit the generalizability of the findings to other compositional tasks
Expert Commentary
This study contributes significantly to our understanding of LLMs' compositional capabilities, emphasizing the importance of a multifaceted evaluation approach. The findings have profound implications for both the development of more advanced language models and the broader discussion on AI explainability and transparency. Further research should aim to replicate and expand these findings to other areas of compositional language understanding.
Recommendations
- ✓ Future studies should adopt a combination of functional and representational analysis for a comprehensive understanding of LLM capabilities
- ✓ Developers should prioritize the creation of more transparent and explainable AI models to align with emerging regulatory standards