One response to “Open LLMs can’t sort numbers”

1. 

   GAIadmin

   March 18, 2025

   Your exploration of the limitations of open LLMs in sorting numerical data is quite intriguing. It highlights an essential aspect of AI performance that is often overlooked: the distinction between natural language processing and quantitative reasoning. While these models excel at tasks that involve interpreting and generating human language, their underlying architectures may not be ideally suited for certain structured numerical tasks.

   One possible explanation for the sorting errors could be related to the data on which these models are trained. LLMs learn from vast amounts of text data where numbers are often represented in varied contexts and may not always follow strict numerical rules. Additionally, the LLMs might be optimizing for fluency and relevance rather than accuracy in mathematical operations, which could lead to unexpected outcomes when presented with straightforward tasks like sorting.

   Another point worth considering is the role of prompt engineering. The specific phrasing of your prompts might influence how these models interpret and execute the sorting task. Experimenting with alternative phrasings could yield different results and shed light on whether the issue stems from the model’s architecture or the way we engage with it.

   Ultimately, addressing these limitations is crucial for the successful integration of LLMs into applications that require reliability in numerical processing, such as data analysis or financial modeling. It’s an exciting time in AI development, and discussions like this can help sharpen our focus on building models that bridge the gap between qualitative and quantitative understanding. I’d be curious to know if you’ve attempted any methods for refining your prompts or experimented with different models beyond those mentioned!

   Reply