Jianfeng Gao
What large language models are as a class of systems — taxonomically, architecturally, and in terms of what they actually inherit from the longer history of neural NLP.
Gao led the 2024 "Large Language Models: A Survey" — one of the broader synthesis papers of the LLM era, organizing the literature not by paper but by the questions that distinguish one architectural lineage from another (decoder-only versus encoder-decoder, instruction-tuning lineages, alignment-training variations, modality extensions). The framing is unusual in that it treats LLMs as a research artifact to be classified and analyzed at the species level, not as a single thing to be cheered for or against. His longer body of work in conversational AI and grounding gives that taxonomical view depth — he has seen which architectural choices survive contact with deployment and which don't.