Large Language Models Lack Understanding of Character Composition of Words
This reveals a critical limitation in LLMs' understanding of basic text units, which could impact their reliability in tasks requiring fine-grained linguistic analysis, though it is incremental as it builds on existing concerns about model capabilities.
The paper examined whether large language models (LLMs) understand character composition in words, finding that most fail at simple tasks humans perform perfectly, indicating a lack of fundamental text comprehension.
Large language models (LLMs) have demonstrated remarkable performances on a wide range of natural language tasks. Yet, LLMs' successes have been largely restricted to tasks concerning words, sentences, or documents, and it remains questionable how much they understand the minimal units of text, namely characters. In this paper, we examine contemporary LLMs regarding their ability to understand character composition of words, and show that most of them fail to reliably carry out even the simple tasks that can be handled by humans with perfection. We analyze their behaviors with comparison to token level performances, and discuss the potential directions for future research.