Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study

The challenges faced by open-source LLMs in data analysis are underscored by their limitations in reasoning-intensive tasks, as highlighted in the recent study on sound symbolism in language models. This study suggests that understanding sound symbolism can enhance multimodal capabilities, which may relate to the strategic planning deficiencies identified in open-source LLMs. Additionally, the Matryoshka Pilot study emphasizes the need for transparency in black-box models, which could further improve reasoning and planning capabilities. Together, these insights suggest that enhancing interaction design and focusing on data quality can significantly improve the performance of open-source LLMs.