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 713 篇，第 3 / 15 页

101. NeurIPS 2024Exploring Context Window of Large Language Models via Decomposed Positional Vectors
102. NeurIPS 2024FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
103. NeurIPS 2024FLAME : Factuality-Aware Alignment for Large Language Models
104. NeurIPS 2024Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
105. NeurIPS 2024Fractal Patterns May Illuminate the Success of Next-Token Prediction
106. NeurIPS 2024Gated Slot Attention for Efficient Linear-Time Sequence Modeling
107. NeurIPS 2024Geometric-Averaged Preference Optimization for Soft Preference Labels
108. NeurIPS 2024Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
109. NeurIPS 2024Grammar-Aligned Decoding
110. NeurIPS 2024Graph-based Uncertainty Metrics for Long-form Language Model Generations
111. NeurIPS 2024GraphVis: Boosting LLMs with Visual Knowledge Graph Integration
112. NeurIPS 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection
113. NeurIPS 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
114. NeurIPS 2024HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction
115. NeurIPS 2024How Do Large Language Models Acquire Factual Knowledge During Pretraining?
116. NeurIPS 2024How do Large Language Models Handle Multilingualism?
117. NeurIPS 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalization
118. NeurIPS 2024HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis
119. NeurIPS 2024I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token
120. NeurIPS 2024IDGen: Item Discrimination Induced Prompt Generation for LLM Evaluation
121. NeurIPS 2024Imitating Language via Scalable Inverse Reinforcement Learning
122. NeurIPS 2024Implicit Optimization Bias of Next-token Prediction in Linear Models
123. NeurIPS 2024Improving Context-Aware Preference Modeling for Language Models
124. NeurIPS 2024Improving Gloss-free Sign Language Translation by Reducing Representation Density
125. NeurIPS 2024In-Context Learning State Vector with Inner and Momentum Optimization
126. NeurIPS 2024InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory
127. NeurIPS 2024InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
128. NeurIPS 2024Information Re-Organization Improves Reasoning in Large Language Models
129. NeurIPS 2024Instance-adaptive Zero-shot Chain-of-Thought Prompting
130. NeurIPS 2024Instruction Tuning With Loss Over Instructions
131. NeurIPS 2024Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity
132. NeurIPS 2024IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering
133. NeurIPS 2024IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons
134. NeurIPS 2024Iterative Reasoning Preference Optimization
135. NeurIPS 2024JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
136. NeurIPS 2024Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting
137. NeurIPS 2024KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
138. NeurIPS 2024KnowGPT: Knowledge Graph based Prompting for Large Language Models
139. NeurIPS 2024Knowledge Circuits in Pretrained Transformers
140. NeurIPS 2024KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
141. NeurIPS 2024LACIE: Listener-Aware Finetuning for Calibration in Large Language Models
142. NeurIPS 2024Language Models as Hierarchy Encoders
143. NeurIPS 2024Large Language Models Must Be Taught to Know What They Don’t Know
144. NeurIPS 2024Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
145. NeurIPS 2024Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
146. NeurIPS 2024Learn To be Efficient: Build Structured Sparsity in Large Language Models
147. NeurIPS 2024Learning Goal-Conditioned Representations for Language Reward Models
148. NeurIPS 2024Learning to Reason via Program Generation, Emulation, and Search
149. NeurIPS 2024LeDex: Training LLMs to Better Self-Debug and Explain Code
150. NeurIPS 2024Lever LM: Configuring In-Context Sequence to Lever Large Vision 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。