Considering the current trends in large language model research, which architectural approach do you think is most effective for a wide range of downstream tasks?
1) Decoder-only models (generative-focused).
2) Encoder-decoder models (sequence-to-sequence).
3) Instruction-tuned models built on top of existing architectures.
Vote for one option and explain the main reason behind your choice—scalability, flexibility, training efficiency, or inference performance.
Which LLM architecture style do you think holds the most promise for future applications?
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I’d put my vote on instruction-tuned models built on top of existing architectures. In my recent work fine-tuning a decoder-only LLM for a fintech chatbot, the instruction layer let us reuse the same base model across sentiment analysis, risk-scoring, and document summarization without retraining each task from scratch. The biggest win was flexibility: a single checkpoint could be prompted for classification, generation, or even structured extraction, which saved a lot of compute and turned the model into a true multi-purpose service. While pure decoder-only or encoder-decoder setups can excel at specific niches, the instruction-tuned route scales most efficiently when you need a broad suite of downstream tasks.