Episode description

In this episode, we tackle an intriguing aspect of artificial intelligence: the challenges large language models (LLMs) face in understanding character composition. Despite their remarkable capabilities in handling complex tasks at the token level, LLMs struggle with tasks that require a deep understanding of how words are composed from characters.

The findings reveal a significant performance gap in these character-focused tasks compared to token-level tasks. LLMs particularly struggle with understanding the position of characters within words, especially when positions are numerically specified.

This limitation is suspected to stem from the training approach of LLMs, which typically treats words as indivisible units (tokens) without considering the underlying character composition.

The episode also delves into potential solutions proposed by experts, including embedding character-level information into word embeddings and employing techniques from visual recognition to simulate human character perception.

Join us as we discuss these innovative approaches to enhancing the understanding of character composition in LLMs and their implications for the development of more nuanced and capable AI systems.

This podcast is based on Shin, A. and Kaneko, K. (2024) Large language models lack understanding of character composition of words, arXiv.org. Available at: https://arxiv.org/abs/2405.11357

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.