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 6 / 33 页

251. ICML 2026Discrete Tilt Matching
252. ICML 2026Disentangling Geometry, Performance, and Training in Language Models
253. ICML 2026Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment
254. ICML 2026DisPPO: Quantile-Based Distributional Reinforcement Learning for Large Language Models
255. ICML 2026Distilling Task-Level Coordination Policies for Generalizable Multi-Agent Cooperation
256. ICML 2026Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
257. ICML 2026Distributed Direct Preference Optimization
258. ICML 2026Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge
259. ICML 2026Distributional Alignment Games for Answer-Level Fine-Tuning
260. ICML 2026Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
261. ICML 2026DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use
262. ICML 2026Diversity Over Frequency: Rethinking Tool Use in Visual Chain-of-Thought Agents
263. ICML 2026Diving into Kronecker Adapters: Component Design Matters
264. ICML 2026dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching
265. ICML 2026DLLMQuant: A Post-Training Quantization Framework Tailored for Diffusion-Based Large Language Models
266. ICML 2026DNACHUNKER: Learnable Tokenization for DNA Language Models
267. ICML 2026Do LLMs “Feel”? Emotion Circuits Discovery and Control
268. ICML 2026Do Sparse Autoencoders Identify Reasoning Features in Language Models?
269. ICML 2026Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models
270. ICML 2026Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
271. ICML 2026DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
272. ICML 2026Does Your Reasoning Model Implicitly Know When to Stop Thinking?
273. ICML 2026Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
274. ICML 2026Don't Force the Fit: Bounded Log-Likelihood Loss for Enhanced Reasoning in Large Language Models
275. ICML 2026Don't Ignore the Tail: Decoupled Distillation Produces Top Maths Students on an Academic Budget
276. ICML 2026DOT-MoE: Differentiable Optimal Transport for MoEfication
277. ICML 2026DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
278. ICML 2026DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents
279. ICML 2026Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
280. ICML 2026Draft-and-Audit Reinforcement Learning for Optimization Modeling
281. ICML 2026DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
282. ICML 2026DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs
283. ICML 2026DSGym: A Standardized and Holistic Framework for Evaluating and Training Data Science Agents
284. ICML 2026DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA
285. ICML 2026DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
286. ICML 2026Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding
287. ICML 2026DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
288. ICML 2026DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
289. ICML 2026Dynamic Multimodal Evaluation via Knowledge-Enhanced Benchmark Evolution
290. ICML 2026Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
291. ICML 2026DynaVieW: Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics
292. ICML 2026E-mem: Multi-Agent Based Episodic Context Reconstruction for LLM Agent Memory
293. ICML 2026E-VAds: An E-commerce Short Videos Understanding Benchmark for MLLMs
294. ICML 2026EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
295. ICML 2026EAKV: An Entropy-Driven Adaptive KV Compression Framework for Long Video Understanding
296. ICML 2026EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance
297. ICML 2026Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models
298. ICML 2026Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
299. ICML 2026EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference
300. ICML 2026ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement 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。