🤖 AI Summary
This work addresses the rapid yet often unstructured evolution of Transformer-based language models by proposing a practical, four-dimensional evaluation framework to distinguish substantive advances from incremental improvements and to guide cross-domain deployment. The framework holistically assesses model architecture, alignment methodologies, energy-efficiency trade-offs, and domain adaptability, encompassing key techniques such as encoder/decoder variants, long-context modeling, mixture-of-experts (MoE), retrieval augmentation, instruction tuning, and preference optimization. Through empirical analysis across vertical domains—including healthcare, finance, and law—the study quantifies the trade-offs between model scale and computational cost, redefines what constitutes an “advanced” model in real-world settings, identifies critical research gaps, and offers actionable guidelines for model selection and deployment.
📝 Abstract
Transformer-based language models have become the default substrate for natural language processing and the pace of new releases has made it hard for practitioners to separate durable ideas from the noise of incremental announcements. This review works at two levels. At the level of mechanism, we organise the main transformer families into a working taxonomy, covering encoder-only, decoder-only, encoder-decoder, long-context, permutation-based, and generator-discriminator variants. We then extend the discussion to post-2023 developments that changed the picture in practice: instruction tuning, reinforcement learning from human feedback, direct preference optimisation, mixture-of-experts scaling, retrieval augmentation and the current flagship model families from OpenAI, Anthropic, Google, Meta, Mistral and DeepSeek. At the level of use, we survey deployments across healthcare, finance, legal, education, customer service, creative writing and scientific work. Based on this we link each to the specific capabilities that make a transformer the appropriate tool. The contribution of this paper is a critical assessment that is based on the survey. We compare architectures on four axes that matter to deployment decisions, we quantify the trade-off between parameter count and energy cost. We also discuss how alignment methods, data provenance and benchmark saturation change what it means to call a model "state of the art". The final section lists the research questions that we think deserve more attention.