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 1,608 篇，第 1 / 33 页

1. ICML 2026“very likely” Means “uncertain”? How LLMs Diverge from Humans in Linguistic Uncertainty Quantification
2. ICML 2026$\phi$-Balancing for Mixture-of-Experts Training
3. ICML 2026$\tau$-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
4. ICML 2026$f$-Divergence Self-Play for Tabular Anomaly Detection via Large Language Models
5. ICML 2026$L^3$: Large Lookup Layers
6. ICML 2026$R^3$DAO: Reactive Recovery and Reconstruction for Long-horizon Data Agent Orchestration
7. ICML 2026$V_0$: A Generalist Value Model for Any Policy at State Zero
8. ICML 20261-Bit Wonder: Improving QAT Performance in the Low-Bit Regime through K-Means Quantization
9. ICML 2026A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn’t)
10. ICML 2026A geometric relation of the error introduced by sampling a language model's output distribution to its internal state
11. ICML 2026A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth
12. ICML 2026A Minimal Agent for Automated Theorem Proving
13. ICML 2026A model of errors in transformers
14. ICML 2026A Narrowing Geometry in Contaminated Reasoning
15. ICML 2026A Tale of Two Graphs: Separating Knowledge Exploration from Outline Structure for Open-Ended Deep Research
16. ICML 2026A Theoretical Framework for Modular Learning of Robust Generative Models
17. ICML 2026A Two-Tier Perspective on Inference-Time Parallelism in Multi-Agent LLM Systems
18. ICML 2026A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions
19. ICML 2026A²RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation
20. ICML 2026A3: an Analytical Low-Rank Approximation Framework for Attention
21. ICML 2026ABCD: All Biases Come Disguised
22. ICML 2026AC-ODM: Actor–Critic Online Data Mixing for Sample-Efficient LLM Pretraining
23. ICML 2026ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
24. ICML 2026AdaGC: Enhancing LLM Pretraining Stability via Adaptive Gradient Clipping
25. ICML 2026AdaHC: Accelerating Multi-Token Prediction with Adaptive Head Chunking with Pipeline Parallelism
26. ICML 2026Adaptive Code Watermarking Through Reinforcement Learning
27. ICML 2026Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
28. ICML 2026AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally
29. ICML 2026Addressing Semantic Blind Spots in Text-to-SQL via Component Pre-generation and AST Matching Rewards
30. ICML 2026Advancing Analytic Class-Incremental Learning through Vision-Language Calibration
31. ICML 2026Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation
32. ICML 2026AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search
33. ICML 2026Adversarial Flow Models
34. ICML 2026Adversarial Latent Embedding Repair for LLM Continual Learning
35. ICML 2026Adversarial Reinforcement Learning for Robust Diffusion Large Language Model Unlearning
36. ICML 2026Adversarial Training for Process Reward Models
37. ICML 2026AffIn-Space: Learning Affine-Invariant Representations for 3D Spatial Understanding with MLLMs
38. ICML 2026Affine-Scaled Attention: Towards Flexible and Stable Transformer Attention
39. ICML 2026Agent Learning via Early Experience
40. ICML 2026Agent Primitives: Reuseable Latent Building Blocks for Multi-Agent Systems
41. ICML 2026Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning
42. ICML 2026Agent-Omit: Adaptive Context Omission for Efficient LLM Agents
43. ICML 2026AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent
44. ICML 2026AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
45. ICML 2026Agentic Proposing: Enhancing Large language Model Reasoning via Compositional Skill Synthesis
46. ICML 2026AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks
47. ICML 2026AgentVocab: Structure-Aware Vocabulary Adaptation for Efficient LLM Agents
48. ICML 2026AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
49. ICML 2026AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
50. ICML 2026AICrypto: Evaluating Cryptography Capabilities of 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。