Instruction and fine-tuning: research map

871 accepted papers on Instruction and fine-tuning in Large language models, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 8 approaches. The busiest year so far is 2026.

Within Large language models, its share held steady from 10.5% in 2023–24 to 9.5% in 2025–26 (208 → 653 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

instruction tuning · instructions · instruction following · 699 papers

Approaches in this cluster:

Efficient LLM fine-tuning systems (219 papers)
Cut memory and compute of fine-tuning through sparsity, projections, and distributed training.
Fine-tuning generalization and delta compression (159 papers)
Study fine-tune deltas, emulation, and mixtures to preserve generality when specializing LLMs.
Fine-tuning for reasoning data (158 papers)
Improve reasoning via tailored losses, scaled instruction data, and efficient distillation.
Safety alignment after fine-tuning (120 papers)
Defend against harmful fine-tuning with post-hoc and in-training safety alignment.
Low-rank adaptation variants (43 papers)
Improve LoRA with adaptive rank, scaling, contextual, and multi-adapter designs.

Most cited and most cited since 2024:

Language Models are Few-Shot Learners (NeurIPS 2020 · 2,966 citations)
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-World Multi-Turn Dialogue (AAAI 2024 · 110 citations)
WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions (ICLR 2024 · 105 citations)
Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024 · 62 citations)

fine tuning · lora · adaptation · 172 papers