Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task
arXiv:2605.31480v1 Announce Type: new Abstract: Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about semantic interpretation, we can distinguish two complementary aspects: establishing what an expression refers to in the world (which we call the Extensional task) and representing its sense in a structured way (which we call the Intensional task). We evaluate LLMs and humans on both tasks in the setting of the Personal Relation Task (Paperno 2022) in which, given a universe of people and their relat
This research provides a current assessment of fundamental LLM capabilities, informing ongoing development and understanding just as these models are being widely integrated into various applications.
Understanding the limits and strengths of large language models in compositional reasoning is crucial for researchers and developers to build more robust and capable AI systems, especially in areas requiring nuanced semantic interpretation.
The paper refines the understanding of LLMs' compositional abilities, suggesting that while they can resolve certain tasks compositionally, it isn't an inherent strength, potentially guiding future architectural improvements and training methodologies.
- · AI researchers
- · NLP developers
- · Companies building agentic AI
- · Overly optimistic projections for current LLM capabilities
Increased focus on explicit compositional training methods and architectural designs for LLMs.
Development of hybrid AI systems combining LLMs with symbolic reasoning modules for complex tasks.
More reliable and less 'hallucinatory' AI agents capable of deeper understanding and interaction.
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