Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions

This paper investigates how language simplification (Normal, Simple, ELI5) affects LLM-generated definitions of homonyms, finding that simplified prompts drastically reduce word sense awareness and completeness. The authors propose a 'Helpful Sense Awareness' metric and demonstrate that fine-tuning improves homonym definition quality across multiple languages.

Large Language Models (LLMs) can provide accurate word definitions and explanations for any context. However, the scope of the definition changes for different target groups, like children or language learners. This is especially relevant for homonyms, words with multiple meanings, where oversimplification might risk information loss by omitting key senses, potentially misleading users who trust LLM outputs. We investigate how simplification impacts homonym definition quality across three target

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