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 25 / 33 页

1201. ICML 2025DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models
1202. ICML 2025Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
1203. ICML 2025DataDecide: How to Predict Best Pretraining Data with Small Experiments
1204. ICML 2025De-mark: Watermark Removal in Large Language Models
1205. ICML 2025Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision
1206. ICML 2025Deliberation in Latent Space via Differentiable Cache Augmentation
1207. ICML 2025Demonstration Selection for In-Context Learning via Reinforcement Learning
1208. ICML 2025Demystifying Long Chain-of-Thought Reasoning
1209. ICML 2025Demystifying Singular Defects in Large Language Models
1210. ICML 2025Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective
1211. ICML 2025Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs
1212. ICML 2025Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
1213. ICML 2025Discriminative Policy Optimization for Token-Level Reward Models
1214. ICML 2025Distillation Scaling Laws
1215. ICML 2025DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs
1216. ICML 2025Diverging Preferences: When do Annotators Disagree and do Models Know?
1217. ICML 2025Diving into Self-Evolving Training for Multimodal Reasoning
1218. ICML 2025DLP: Dynamic Layerwise Pruning in Large Language Models
1219. ICML 2025Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models
1220. ICML 2025DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning
1221. ICML 2025Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training
1222. ICML 2025DPO Meets PPO: Reinforced Token Optimization for RLHF
1223. ICML 2025DS-VLM: Diffusion Supervision Vision Language Model
1224. ICML 2025DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
1225. ICML 2025Earley-Driven Dynamic Pruning for Efficient Structured Decoding
1226. ICML 2025Efficient Long Context Fine-tuning with Chunk Flow
1227. ICML 2025EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
1228. ICML 2025Eliciting Language Model Behaviors with Investigator Agents
1229. ICML 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
1230. ICML 2025Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective
1231. ICML 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
1232. ICML 2025Emergent Response Planning in LLMs
1233. ICML 2025Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
1234. ICML 2025Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model
1235. ICML 2025Enhancing Decision-Making of Large Language Models via Actor-Critic
1236. ICML 2025EpiCoder: Encompassing Diversity and Complexity in Code Generation
1237. ICML 2025Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators
1238. ICML 2025Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
1239. ICML 2025Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
1240. ICML 2025EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration
1241. ICML 2025Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective
1242. ICML 2025EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
1243. ICML 2025Explicit Preference Optimization: No Need for an Implicit Reward Model
1244. ICML 2025Exploiting Presentative Feature Distributions for Parameter-Efficient Continual Learning of Large Language Models
1245. ICML 2025FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees
1246. ICML 2025Fast Large Language Model Collaborative Decoding via Speculation
1247. ICML 2025Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
1248. ICML 2025FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation
1249. ICML 2025Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
1250. ICML 2025FlatQuant: Flatness Matters for LLM Quantization

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。