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 5 / 15 页

201. NeurIPS 2024Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization
202. NeurIPS 2024Policy Improvement using Language Feedback Models
203. NeurIPS 2024Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure
204. NeurIPS 2024Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
205. NeurIPS 2024Preference Learning Algorithms Do Not Learn Preference Rankings
206. NeurIPS 2024Probing the Decision Boundaries of In-context Learning in Large Language Models
207. NeurIPS 2024Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
208. NeurIPS 2024Protecting Your LLMs with Information Bottleneck
209. NeurIPS 2024PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
210. NeurIPS 2024QBB: Quantization with Binary Bases for LLMs
211. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
212. NeurIPS 2024Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing
213. NeurIPS 2024QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
214. NeurIPS 2024QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
215. NeurIPS 2024Questioning the Survey Responses of Large Language Models
216. NeurIPS 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
217. NeurIPS 2024Reasons and Solutions for the Decline in Model Performance after Editing
218. NeurIPS 2024Recursive Introspection: Teaching Language Model Agents How to Self-Improve
219. NeurIPS 2024Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models
220. NeurIPS 2024Reflective Multi-Agent Collaboration based on Large Language Models
221. NeurIPS 2024ReFT: Representation Finetuning for Language Models
222. NeurIPS 2024Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs
223. NeurIPS 2024Representation Noising: A Defence Mechanism Against Harmful Finetuning
224. NeurIPS 2024Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
225. NeurIPS 2024Reranking Laws for Language Generation: A Communication-Theoretic Perspective
226. NeurIPS 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search
227. NeurIPS 2024Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
228. NeurIPS 2024RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
229. NeurIPS 2024RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models
230. NeurIPS 2024Rule Based Rewards for Language Model Safety
231. NeurIPS 2024SafeWorld: Geo-Diverse Safety Alignment
232. NeurIPS 2024SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain
233. NeurIPS 2024Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
234. NeurIPS 2024Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
235. NeurIPS 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
236. NeurIPS 2024Scaling Sign Language Translation
237. NeurIPS 2024SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge
238. NeurIPS 2024Segmenting Watermarked Texts From Language Models
239. NeurIPS 2024SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
240. NeurIPS 2024SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
241. NeurIPS 2024Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
242. NeurIPS 2024Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
243. NeurIPS 2024Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
244. NeurIPS 2024Semantics and Spatiality of Emergent Communication
245. NeurIPS 2024Sequoia: Scalable and Robust Speculative Decoding
246. NeurIPS 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
247. NeurIPS 2024SimPO: Simple Preference Optimization with a Reference-Free Reward
248. NeurIPS 2024SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models
249. NeurIPS 2024SlimGPT: Layer-wise Structured Pruning for Large Language Models
250. NeurIPS 2024SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining

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。