![[D]2025 Year in Review: The old methods quietly solving problems the new ones can't](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-6356-300x171.png)
In a reflection on the evolution of language models and AI, the enduring relevance of older methodologies is highlighted, especially as they address issues that newer approaches struggle with. Despite the advancements in transformer models, challenges like efficiently solving problems and handling linguistic variations remain. Techniques such as Hidden Markov Models (HMMs), Viterbi algorithms, and n-gram smoothing are resurfacing as effective solutions for these persistent issues. These older methods offer robust frameworks for tasks where modern models, like LLMs, may falter due to their limitations in covering the full spectrum of linguistic diversity. Understanding the strengths of both old and new techniques is crucial for developing more reliable AI systems.
