LLMs Are Still Toxic, Stuck in the Past, and Bad at Math (www.eyosias.dev)

Recent evaluations of large language models (LLMs) reveal persistent issues with their performance, even after nearly four years since the launch of ChatGPT. Key problems include poor mathematical abilities, a lack of updated knowledge beyond their training cutoffs, limited short-term memory due to fixed context windows, and the prevalence of toxic outputs that reflect harmful internet content. While developers have introduced workarounds, such as tool calling to enhance computation and Retrieval-Augmented Generation (RAG) for updated knowledge, these limitations remain inherent in the models themselves. These ongoing challenges are particularly significant for the AI/ML community as they highlight the current state of LLMs and the necessity for continual innovation. The introduction of mechanisms like MCP for standardized tool access and external computational support signals progress but emphasizes that foundational model weaknesses persist. As developers navigate these tools, understanding these constraints is crucial for leveraging AI effectively. The conversation surrounding LLMs is shifting towards finding effective solutions for these issues, which will be essential for future advancements in this rapidly evolving field.