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 415 篇，第 3 / 9 页

101. NeurIPS 2023Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
102. NeurIPS 2023Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
103. NeurIPS 2023Easy Bayesian Transfer Learning with Informative Priors
104. NeurIPS 2023Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing
105. NeurIPS 2023Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination
106. NeurIPS 2023Efficient Beam Tree Recursion
107. NeurIPS 2023Efficient Diffusion Policies For Offline Reinforcement Learning
108. NeurIPS 2023Efficient Equivariant Transfer Learning from Pretrained Models
109. NeurIPS 2023Efficiently incorporating quintuple interactions into geometric deep learning force fields
110. NeurIPS 2023EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks
111. NeurIPS 2023EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis
112. NeurIPS 2023Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
113. NeurIPS 2023Emergent and Predictable Memorization in Large Language Models
114. NeurIPS 2023EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
115. NeurIPS 2023Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
116. NeurIPS 2023Epistemic Neural Networks
117. NeurIPS 2023Estimating Koopman operators with sketching to provably learn large scale dynamical systems
118. NeurIPS 2023Evaluating and Inducing Personality in Pre-trained Language Models
119. NeurIPS 2023Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
120. NeurIPS 2023Evaluating Neuron Interpretation Methods of NLP Models
121. NeurIPS 2023Evaluating Open-QA Evaluation
122. NeurIPS 2023Evaluating the Moral Beliefs Encoded in LLMs
123. NeurIPS 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
124. NeurIPS 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
125. NeurIPS 2023EvoPrompting: Language Models for Code-Level Neural Architecture Search
126. NeurIPS 2023Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
127. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
128. NeurIPS 2023Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
129. NeurIPS 2023Exploring Question Decomposition for Zero-Shot VQA
130. NeurIPS 2023Exposing Attention Glitches with Flip-Flop Language Modeling
131. NeurIPS 2023Extracting Reward Functions from Diffusion Models
132. NeurIPS 2023Fairness-guided Few-shot Prompting for Large Language Models
133. NeurIPS 2023Faith and Fate: Limits of Transformers on Compositionality
134. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
135. NeurIPS 2023Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention
136. NeurIPS 2023Fast Attention Requires Bounded Entries
137. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
138. NeurIPS 2023Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
139. NeurIPS 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
140. NeurIPS 2023Fine-Tuning Language Models with Just Forward Passes
141. NeurIPS 2023Flow: Per-instance Personalized Federated Learning
142. NeurIPS 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
143. NeurIPS 2023From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader
144. NeurIPS 2023Generating Images with Multimodal Language Models
145. NeurIPS 2023Generating QM1B with PySCF$_{\text{IPU}}$
146. NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
147. NeurIPS 2023Geometry-Aware Adaptation for Pretrained Models
148. NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
149. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
150. NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

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。