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 404 篇，第 2 / 9 页

51. ICML 2025CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration
52. ICML 2025Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
53. ICML 2025CLOVER: Cross-Layer Orthogonal Vectors Pruning
54. ICML 2025CodeIO: Condensing Reasoning Patterns via Code Input-Output Prediction
55. ICML 2025Collapse or Thrive: Perils and Promises of Synthetic Data in a Self-Generating World
56. ICML 2025CoMemo: LVLMs Need Image Context with Image Memory
57. ICML 2025Communicating Activations Between Language Model Agents
58. ICML 2025CommVQ: Commutative Vector Quantization for KV Cache Compression
59. ICML 2025Compositional Causal Reasoning Evaluation in Language Models
60. ICML 2025Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
61. ICML 2025ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization
62. ICML 2025Controlling Large Language Model with Latent Action
63. ICML 2025Core Context Aware Transformers for Long Context Language Modeling
64. ICML 2025CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models
65. ICML 2025Cost-efficient Collaboration between On-device and Cloud Language Models
66. ICML 2025CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models
67. ICML 2025CRANE: Reasoning with constrained LLM generation
68. ICML 2025Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM’s Reasoning Capability
69. ICML 2025CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization
70. ICML 2025DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models
71. ICML 2025Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
72. ICML 2025DataDecide: How to Predict Best Pretraining Data with Small Experiments
73. ICML 2025De-mark: Watermark Removal in Large Language Models
74. ICML 2025Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision
75. ICML 2025Deliberation in Latent Space via Differentiable Cache Augmentation
76. ICML 2025Demonstration Selection for In-Context Learning via Reinforcement Learning
77. ICML 2025Demystifying Long Chain-of-Thought Reasoning
78. ICML 2025Demystifying Singular Defects in Large Language Models
79. ICML 2025Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective
80. ICML 2025Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs
81. ICML 2025Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
82. ICML 2025Discriminative Policy Optimization for Token-Level Reward Models
83. ICML 2025Distillation Scaling Laws
84. ICML 2025DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs
85. ICML 2025Diverging Preferences: When do Annotators Disagree and do Models Know?
86. ICML 2025Diving into Self-Evolving Training for Multimodal Reasoning
87. ICML 2025DLP: Dynamic Layerwise Pruning in Large Language Models
88. ICML 2025Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models
89. ICML 2025DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning
90. ICML 2025Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training
91. ICML 2025DPO Meets PPO: Reinforced Token Optimization for RLHF
92. ICML 2025DS-VLM: Diffusion Supervision Vision Language Model
93. ICML 2025DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
94. ICML 2025Earley-Driven Dynamic Pruning for Efficient Structured Decoding
95. ICML 2025Efficient Long Context Fine-tuning with Chunk Flow
96. ICML 2025EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
97. ICML 2025Eliciting Language Model Behaviors with Investigator Agents
98. ICML 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
99. ICML 2025Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective
100. ICML 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

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。