aiqbench analysis
This research investigates how language models internally represent multi-word units like phrasal verbs, demonstrating that frequency and predictability drive holistic storage in both text-based LLMs and audio speech recognition systems. The findings provide empirical support for usage-based theories of language by confirming that these models learn distinct representations rather than relying solely on abstract compositional rules.
The Holistic Storage of Verb+Up Phrases in Text-based and Audio-based Language Models
arXiv:2606.13993v1 Announce Type: new Abstract: A crucial aspect of linguistic capability is the ability to trade off between stored representations and abstract knowledge: one must retrieve learned representations, but also generate novel ones by applying productive rules. While recent work has examined abstract knowledge in language models, holistic storage of multi-word units has received far less attention. We probe internal representations in text-based LLMs and an ASR model, testing whether V+up phrasal verbs develop distinct representations as a function of frequency and predictability. All models show evidence of holistic storage driven by frequency and predictability, further supporting usage-based theories of language.
Abstract:A crucial aspect of linguistic capability is the ability to trade off between stored representations and abstract knowledge: one must retrieve learned representations, but also generate novel ones by applying productive rules. While recent work has examined abstract knowledge in language models, holistic storage of multi-word units has received far less attention. We probe internal representations in text-based LLMs and an ASR model, testing whether V+up phrasal verbs develop distinct representations as a function of frequency and predictability. All models show evidence of holistic storage driven by frequency and predictability, further supporting usage-based theories of language.
Computation and Language (cs.CL)
arXiv:2606.13993 [cs.CL]
(or arXiv:2606.13993v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13993 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 191 words
Related coverage
The Holistic Storage of Verb+Up Phrases in Text-based and Audio-based Language Models
arXiv · Aug 4, 2026

Agentic conversational video intelligence built on AWS
AWS · Sep 23, 2026

Evaluate your Amazon Nova Sonic voice agent at scale, no microphone required
AWS · Jun 8, 2026

Hands-free first notice of loss: Using Strands Agents and Amazon Bedrock AgentCore Browser Tool for intelligent claims intake
AWS · Jun 9, 2026