深度学习 · large language models论文趋势 2023–2026
ICML/NeurIPS 深度学习方向:large_language_models
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 2,334 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 415 | 0 | 73 | 0 | 488 |
| 2024 | 298 | 0 | 0 | 0 | 298 |
| 2025 | 0 | 13 | 404 | 0 | 417 |
| 2026 | 0 | 0 | 1,131 | 0 | 1,131 |
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 2,334 篇,第 42 / 47 页
- NeurIPS 2023Fairness-guided Few-shot Prompting for Large Language Models
- NeurIPS 2023Faith and Fate: Limits of Transformers on Compositionality
- NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
- NeurIPS 2023Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention
- NeurIPS 2023Fast Attention Requires Bounded Entries
- NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
- NeurIPS 2023Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
- NeurIPS 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
- NeurIPS 2023Fine-Tuning Language Models with Just Forward Passes
- NeurIPS 2023Flow: Per-instance Personalized Federated Learning
- NeurIPS 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
- NeurIPS 2023From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader
- NeurIPS 2023Generating Images with Multimodal Language Models
- NeurIPS 2023Generating QM1B with PySCF$_{\text{IPU}}$
- NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
- NeurIPS 2023Geometry-Aware Adaptation for Pretrained Models
- NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
- NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
- NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
- NeurIPS 2023Global Optimality in Bivariate Gradient-based DAG Learning
- NeurIPS 2023Goal Driven Discovery of Distributional Differences via Language Descriptions
- NeurIPS 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
- NeurIPS 2023Gradient-Based Feature Learning under Structured Data
- NeurIPS 2023Grammar Prompting for Domain-Specific Language Generation with Large Language Models
- NeurIPS 2023GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
- NeurIPS 2023Grassmann Manifold Flows for Stable Shape Generation
- NeurIPS 2023Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
- NeurIPS 2023Guiding Large Language Models via Directional Stimulus Prompting
- NeurIPS 2023Guiding The Last Layer in Federated Learning with Pre-Trained Models
- NeurIPS 2023H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
- NeurIPS 2023High dimensional, tabular deep learning with an auxiliary knowledge graph
- NeurIPS 2023High-dimensional Contextual Bandit Problem without Sparsity
- NeurIPS 2023HIQL: Offline Goal-Conditioned RL with Latent States as Actions
- NeurIPS 2023HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count
- NeurIPS 2023Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space
- NeurIPS 2023HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
- NeurIPS 2023Human spatiotemporal pattern learning as probabilistic program synthesis
- NeurIPS 2023Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
- NeurIPS 2023HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
- NeurIPS 2023HyTrel: Hypergraph-enhanced Tabular Data Representation Learning
- NeurIPS 2023IBA: Towards Irreversible Backdoor Attacks in Federated Learning
- NeurIPS 2023Im-Promptu: In-Context Composition from Image Prompts
- NeurIPS 2023Image Captioners Are Scalable Vision Learners Too
- NeurIPS 2023Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models
- NeurIPS 2023Implicit Variational Inference for High-Dimensional Posteriors
- NeurIPS 2023Improving Adversarial Transferability via Intermediate-level Perturbation Decay
- NeurIPS 2023Improving CLIP Training with Language Rewrites
- NeurIPS 2023Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
- NeurIPS 2023In-Context Impersonation Reveals Large Language Models' Strengths and Biases
- NeurIPS 2023In-Context Learning Unlocked for Diffusion Models
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查深度学习 · large language models在 ICML 2023–2026 的论文趋势,哪一年涨得最快?
AI 会调用的工具:get_topic_stats {"topic": "dl-large-language-models"}
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。
「深度学习」下的其他主题
- 扩散模型 6,900
- 深度学习 · generative models and autoencoders 4,314
- 深度学习 · self supervised learning 1,958
- 深度学习 · graph neural networks 1,648
- 深度学习 · optimization 953
- 深度学习 · representation learning 802
- 深度学习 · probabilistic methods 637
- 深度学习 · foundation models 536
- 深度学习 · causal inference 316
- 深度学习 · transfer learning 197