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 5 / 33 页

201. ICML 2026Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
202. ICML 2026Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
203. ICML 2026CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing
204. ICML 2026Criterion-Conditional In-Context Learning: Evaluating Criterion-Shift Adaptation in Vision-Language Models
205. ICML 2026CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective
206. ICML 2026Critique-Guided Distillation for Robust Reasoning via Refinement
207. ICML 2026CSPLoRA: Confidence-Guided Structure Planning for Low-Rank Adaptation
208. ICML 2026CUARewardBench: A Benchmark for Evaluating Reward Models for Computer-Using Agents
209. ICML 2026Curating the Future: A Scalable Recipe for Training Open-Ended Forecasters
210. ICML 2026Cure-SFT: Diagnostic-Guided Data Curation for Instruction Tuning
211. ICML 2026Curriculum Reinforcement Learning for Black-Box Prompt Tuning via Large Language Models
212. ICML 2026Curriculum-Guided Layer Scaling for Language Model Pretraining
213. ICML 2026CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception
214. ICML 2026D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
215. ICML 2026D-FUSEr: Diverse Failure, Unified Success via Error-Distribution Shaping in LLM Reasoning
216. ICML 2026D$^3$: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training
217. ICML 2026D²Evo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning
218. ICML 2026d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation
219. ICML 2026DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
220. ICML 2026DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding
221. ICML 2026DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
222. ICML 2026Data Difficulty and the Generalization–Extrapolation Tradeoff in LLM Fine-Tuning
223. ICML 2026Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique
224. ICML 2026daVinci-Dev: Agent-native Mid-training for Software Engineering
225. ICML 2026DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding
226. ICML 2026Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging
227. ICML 2026DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions
228. ICML 2026Decompose, Structure, and Repair: A Neuro-Symbolic Framework for Autoformalization via Operator Trees
229. ICML 2026Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training
230. ICML 2026Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
231. ICML 2026Deep Pre-Alignment for VLMs
232. ICML 2026Deep sequence models tend to memorize geometrically; it is unclear why
233. ICML 2026DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
234. ICML 2026DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories
235. ICML 2026Demystifying Entropy Control in LLM RL Training: Theoretical Analysis and Dynamic Scheduling
236. ICML 2026DenseSteer: Steering Small Language Models towards Dense Math Reasoning
237. ICML 2026Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised Representation
238. ICML 2026Detecting Contextual Hallucinations in Large Language Models with Frequency-Aware Attention
239. ICML 2026Detecting Errors in AI-Generated Annotations: When and Why Semantic Neighbors Help
240. ICML 2026Deterministic Differentiable Structured Pruning for Large Language Models
241. ICML 2026Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatch
242. ICML 2026DFlash: Block Diffusion for Flash Speculative Decoding
243. ICML 2026Differential syntactic and semantic encoding in LLMs
244. ICML 2026Diffract: Spectral View of LLM Domain Adaptation
245. ICML 2026DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
246. ICML 2026DiffuReason: Enhancing Reasoning Ability for Diffusion Language Models via Monte Carlo Tree Search
247. ICML 2026Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs
248. ICML 2026Discounted Beta–Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
249. ICML 2026Discovering Interpretable Algorithms by Decompiling Transformers to RASP
250. ICML 2026Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation

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。