深度学习 · large language models论文趋势 2023–2026

主要结论

• 深度学习 · large language models论文从 2023 年的 488 篇增长到 2026 年的 1,131 篇，三年间扩大到约 2.3 倍。
• ICML 是发深度学习 · large language models论文最多的会议，2023–2026 年共 1,608 篇，占四个会议总量的 69%。
• 交叉方向里涨得最快的是AI 智能体：同时属于两个主题的论文从 2023 年的 5 篇增至 2026 年的 184 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 3；同组增长最快的扩散模型是 4.1 倍，深度学习 · large language models是 2.3 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• ICML 2026“very likely” Means “uncertain”? How LLMs Diverge from Humans in Linguistic Uncertainty Quantification
• ICML 2025(How) Do Language Models Track State?
• ICLR 2025AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models
• NeurIPS 2024$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$
• NeurIPS 20233D-LLM: Injecting the 3D World into Large Language Models
• ICML 2023Are Large Kernels Better Teachers than Transformers for ConvNets?
• ICML 2026$f$-Divergence Self-Play for Tabular Anomaly Detection via Large Language Models
• ICML 2025A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
• ICLR 2025Emergence of a High-Dimensional Abstraction Phase in Language Transformers
• NeurIPS 2024$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning
• NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
• ICML 2023Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
• ICML 2026$L^3$: Large Lookup Layers
• ICML 2025Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies
• ICLR 2025Holistically Evaluating the Environmental Impact of Creating Language Models
• NeurIPS 2024A distributional simplicity bias in the learning dynamics of transformers
• NeurIPS 2023A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints
• ICML 2023Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
• ICML 2026$V_0$: A Generalist Value Model for Any Policy at State Zero
• ICML 2025Accelerating Unbiased LLM Evaluation via Synthetic Feedback

全部深度学习 · large language models论文

共 713 篇，第 2 / 15 页

51. NeurIPS 2024Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
52. NeurIPS 2024COLD: Causal reasOning in cLosed Daily activities
53. NeurIPS 2024Compact Language Models via Pruning and Knowledge Distillation
54. NeurIPS 2024Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models
55. NeurIPS 2024Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
56. NeurIPS 2024ConStat: Performance-Based Contamination Detection in Large Language Models
57. NeurIPS 2024ContextCite: Attributing Model Generation to Context
58. NeurIPS 2024CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning
59. NeurIPS 2024Cost-efficient Knowledge-based Question Answering with Large Language Models
60. NeurIPS 2024Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
61. NeurIPS 2024CriticEval: Evaluating Large-scale Language Model as Critic
62. NeurIPS 2024Cross-model Control: Improving Multiple Large Language Models in One-time Training
63. NeurIPS 2024D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
64. NeurIPS 2024D-LLM: A Token Adaptive Computing Resource Allocation Strategy for Large Language Models
65. NeurIPS 2024DAPE: Data-Adaptive Positional Encoding for Length Extrapolation
66. NeurIPS 2024DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
67. NeurIPS 2024Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions
68. NeurIPS 2024Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
69. NeurIPS 2024DDK: Distilling Domain Knowledge for Efficient Large Language Models
70. NeurIPS 2024Decoding-Time Language Model Alignment with Multiple Objectives
71. NeurIPS 2024Decompose, Analyze and Rethink: Solving Intricate Problems with Human-like Reasoning Cycle
72. NeurIPS 2024Deep Bayesian Active Learning for Preference Modeling in Large Language Models
73. NeurIPS 2024Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models
74. NeurIPS 2024Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
75. NeurIPS 2024DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning
76. NeurIPS 2024DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ
77. NeurIPS 2024DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion
78. NeurIPS 2024DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
79. NeurIPS 2024Discovering Preference Optimization Algorithms with and for Large Language Models
80. NeurIPS 2024Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models
81. NeurIPS 2024Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
82. NeurIPS 2024Do LLMs Build World Representations? Probing Through the Lens of State Abstraction
83. NeurIPS 2024DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation
84. NeurIPS 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
85. NeurIPS 2024EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas
86. NeurIPS 2024Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
87. NeurIPS 2024Efficient Adversarial Training in LLMs with Continuous Attacks
88. NeurIPS 2024Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
89. NeurIPS 2024EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization
90. NeurIPS 2024Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning
91. NeurIPS 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Data
92. NeurIPS 2024Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
93. NeurIPS 2024Enhancing Large Language Models through Adaptive Tokenizers
94. NeurIPS 2024Enhancing Large Vision Language Models with Self-Training on Image Comprehension
95. NeurIPS 2024Enhancing LLM’s Cognition via Structurization
96. NeurIPS 2024Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control
97. NeurIPS 2024Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
98. NeurIPS 2024Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
99. NeurIPS 2024Entity Alignment with Noisy Annotations from Large Language Models
100. NeurIPS 2024Explaining Datasets in Words: Statistical Models with Natural Language Parameters

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · large language models论文？

共 2,334 篇，按年份分别是 2023 年 488 篇、2024 年 298 篇、2025 年 417 篇、2026 年 1,131 篇。

NeurIPS 2024 有多少篇深度学习 · large language models论文？

NeurIPS 2024 有 298 篇深度学习 · large language models论文，上一届是 415 篇。

ICLR 2025 有多少篇深度学习 · large language models论文？

ICLR 2025 有 13 篇深度学习 · large language models论文。

ICML 2026 有多少篇深度学习 · large language models论文？

ICML 2026 有 1,131 篇深度学习 · large language models论文，上一届是 404 篇。

哪个会议的深度学习 · large language models论文最多？

ICML，2023–2026 年共 1,608 篇。