Modal Sense Disambiguation in Language Models: The Role of Context, Training Domain, and Input Frequency

Key points

• Modals such as may and must can take on different senses depending on discourse context, alternating between root interpretations (e.g., permission, obligation) and epistemic interpretations (e.g., knowledge, belief).1
• While modals are known to be notoriously difficult for children to acquire, limited research exists on whether language models (LMs) can reliably distinguish between different modal senses.1
• We show that larger LMs trained on diverse text successfully leverage additional contextual information when available.1
• Smaller developmentally plausible LMs, including those trained on child-directed language (CDL), fail to show consistent sensitivity to modal sense distinctions and do not benefit from context to the same degree.1

Sources (1)

• [1]Modal Sense Disambiguation in Language Models: The Role of Context, Training Domain, and Input Frequency
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:31 AM

  Specifically, we investigate whether LMs can leverage contextual information to disambiguate modal senses and whether their performance reflects the distribution of modal senses in their training data.

  Modals such as may and must can take on different senses depending on discourse context, alternating between root interpretations (e.g., permission, obligation) and epistemic interpretations (e.g., knowledge, belief).

Extractive summary: sentences quoted from the sources.