# The Holistic Storage of Verb+Up Phrases in Text-based and Audio-based Language Models

arXiv: https://arxiv.org/abs/2606.13993 · HTML: https://mlsanity.com/paper/2606.13993/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens](https://mlsanity.com/paper/2401.17377/) (2024)
- [How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN](https://mlsanity.com/paper/2111.09509/) (2021)
- [Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs](https://mlsanity.com/paper/2403.19827/) (2024)
- [Both Direct and Indirect Evidence Contribute to Dative Alternation Preferences in Language Models](https://mlsanity.com/paper/2503.20850/) (2025)
- [Subject Verb Agreement Error Patterns in Meaningless Sentences: Humans vs. BERT](https://mlsanity.com/paper/2209.10538/) (2022)
- [On the Interpretability of Whisper Encodings Using Sparse Autoencoders](https://mlsanity.com/paper/2605.12225/) (2026)

## Abstract (arXiv)

One of the most central aspects of language processing is the ability to trade off between stored representations and abstract knowledge: one must retrieve stored representations, but also generate novel ones by applying productive rules. While recent work has examined abstract knowledge in language models, holistic storage has received far less attention. We probe internal representations in both 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.