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.
The paper is published as research into the internal mechanisms of AI models becomes increasingly sophisticated, moving beyond mere performance metrics to understanding underlying linguistic representations.
Understanding how language models store and generate linguistic units like 'Verb+Up' phrases can reveal the fundamental nature of their 'understanding' and potential limitations or capabilities in complex language tasks.
This research contributes to a deeper, more scientific understanding of AI model 'intelligence,' potentially informing the design of more human-like or efficient language models rather than just black-box improvements.
- · AI researchers
- · Linguists
- · Natural Language Processing (NLP) developers
- · Opaquely developed language models
It provides insights into how text-based and audio-based LLMs store and process multi-word units, specifically 'Verb+Up' phrases.
This understanding could lead to more robust and generalized language models that better handle idiomatic expressions and complex phrasal structures.
Improved linguistic capabilities might accelerate the development of more nuanced and context-aware AI agents, impacting various white-collar workflows.
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