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 4 / 47 页
- ICML 2026Causal Dependency-Aware Unsupervised Routing for Large Reasoning Models
- ICML 2026Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
- ICML 2026CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
- ICML 2026Causally Evaluating the Learnability of Formal Language Tasks
- ICML 2026CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG
- ICML 2026CauSciBench: Can LLMs Automate Causal Inference in Real-World Scientific Research?
- ICML 2026Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE
- ICML 2026CGSVD: Cascaded Granular Singular Value Decomposition for Large Language Model Compression
- ICML 2026Characterizing, Evaluating, and Optimizing Complex Reasoning
- ICML 2026Chiral Symmetry Breaking in Transformers: A Group-Equivariant Framework for Addressing the Reversal Curse via Adjoint Manifold Mappings
- ICML 2026Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Vision-Language Models
- ICML 2026CircuitPrint: Mechanistic Circuit Fingerprints for Large Language Models
- ICML 2026CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
- ICML 2026Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation
- ICML 2026CLIMB: Taming the LoRA Residency Cliff in Multi-LoRA Serving
- ICML 2026Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals
- ICML 2026CoDA-Bench: Can Code Agents Handle Data-Intensive Tasks?
- ICML 2026CodeChemist: Test-Time Scaling for Low-Resource Code Generation via Functional Knowledge Transfer
- ICML 2026Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner
- ICML 2026CofactGVR: Counterfactual Intervention for Grounded Visual Reasoning
- ICML 2026Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement
- ICML 2026CoMem: Context Management with A Decoupled Long-Context Model
- ICML 2026Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs
- ICML 2026ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
- ICML 2026Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
- ICML 2026Computational Arbitrage in AI Model Markets
- ICML 2026Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
- ICML 2026Compute Where it Counts: Self Optimizing Language Models
- ICML 2026Concept Heterogeneity-aware Representation Steering
- ICML 2026Confidence is Not Universal: Task-Dependent Calibration and Emergent Behavior in LLMs
- ICML 2026Configurable Reward Model for Balanced Safety Alignment
- ICML 2026Conformal Thinking: Risk Control for Reasoning on a Compute Budget
- ICML 2026ConFu: Contemplate the Future for Better Speculative Sampling
- ICML 2026ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure
- ICML 2026ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving
- ICML 2026Context Tuning for In-Context Optimization
- ICML 2026Context-level Language Modeling by Learning Predictive Context Embeddings
- ICML 2026Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
- ICML 2026Contextualized Privacy Defense for LLM Agents
- ICML 2026Continuous Diffusion Models Can Obey Formal Syntax
- ICML 2026Contrastive Weak-to-Strong Generalization
- ICML 2026Convex Dataset Valuation for Post-Training
- ICML 2026Convex Optimization for Alignment and Preference Learning on a Single GPU
- ICML 2026ConvexBench: Can LLMs Recognize Convex Functions?
- ICML 2026CoPE: A Framework for Optimizing Coordination between Planning and Execution in LLM-based Agents
- ICML 2026CoPE: Continual Probe-guided Expansion for Large Vision-Language Models
- ICML 2026CORE: Context-Robust Remasking for Diffusion Language Models
- ICML 2026CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems
- ICML 2026Correcting in Hindsight: Editing Past Key-Value States for Robust LLM Reasoning
- ICML 2026Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
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