Research map / Large language models
Alignment and preferences: research map
665 accepted papers on Alignment and preferences in Large language models, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 7 approaches. The busiest year so far is 2026.
Within Large language models, its share grew from 6.6% in 2023–24 to 7.6% in 2025–26 (131 → 522 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
feedback · human · rlhf · 416 papers
Approaches in this cluster:
- Steering and correcting LLM behavior (127 papers)
Align LLMs through representation editing, influence functions, and intrinsic corpus signals. - Reward modeling for diverse preferences (90 papers)
Improve RLHF reward models with language feedback, diverse preferences, and error analysis. - LLM-as-judge and feedback data (111 papers)
Collect scaled AI feedback and study human-versus-LLM judgment in evaluation. - Reward model design and preference models (88 papers)
Combine, actively label, and generalize reward and preference models beyond Bradley-Terry.
Most cited and most cited since 2024:
- Training language models to follow instructions with human feedback (NeurIPS 2022 · 1,219 citations)
- Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (NeurIPS 2023 · 810 citations)
- Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference (ICML 2024 · 103 citations)
- RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (ICML 2024 · 69 citations)
preference optimization · dpo · direct preference · 249 papers
Approaches in this cluster:
- Robust and calibrated DPO (118 papers)
Make direct preference optimization robust to noisy preferences and distribution shift through calibration and data selection. - Active and token-level preference learning (58 papers)
Extend DPO with token-level objectives, reward augmentation, and active preference collection. - DPO design choices (73 papers)
Study data, scheduling, dynamic beta, and multi-reference variants of direct preference optimization.
Most cited and most cited since 2024:
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model (NeurIPS 2023 · 418 citations)
- Diffusion Model Alignment Using Direct Preference Optimization (CVPR 2024 · 154 citations)
- SimPO: Simple Preference Optimization with a Reference-Free Reward (NeurIPS 2024 · 39 citations)
- On Softmax Direct Preference Optimization for Recommendation (NeurIPS 2024 · 23 citations)
Related topics in Large language models
- Reasoning and chain of thought (1,557)
- Instruction and fine-tuning (871)
- Retrieval and knowledge (1,064)
- Agents, code and math (1,722)
- AI and society (827)
- Language, safety and interpretability (2,928)