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 298 篇，第 6 / 6 页

251. NeurIPS 2024Smoothie: Label Free Language Model Routing
252. NeurIPS 2024SnapKV: LLM Knows What You are Looking for Before Generation
253. NeurIPS 2024SpaceByte: Towards Deleting Tokenization from Large Language Modeling
254. NeurIPS 2024SparseLLM: Towards Global Pruning of Pre-trained Language Models
255. NeurIPS 2024SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices
256. NeurIPS 2024Spectral Editing of Activations for Large Language Model Alignment
257. NeurIPS 2024Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration
258. NeurIPS 2024Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
259. NeurIPS 2024Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
260. NeurIPS 2024Stealth edits to large language models
261. NeurIPS 2024StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
262. NeurIPS 2024StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses
263. NeurIPS 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
264. NeurIPS 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
265. NeurIPS 2024Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale
266. NeurIPS 2024TableRAG: Million-Token Table Understanding with Language Models
267. NeurIPS 2024Tackling Uncertain Correspondences for Multi-Modal Entity Alignment
268. NeurIPS 2024TAIA: Large Language Models are Out-of-Distribution Data Learners
269. NeurIPS 2024Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
270. NeurIPS 2024Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
271. NeurIPS 2024The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
272. NeurIPS 2024Theoretical Analysis of Weak-to-Strong Generalization
273. NeurIPS 2024Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
274. NeurIPS 2024Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
275. NeurIPS 2024Toxicity Detection for Free
276. NeurIPS 2024Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning
277. NeurIPS 2024Transformers need glasses! Information over-squashing in language tasks
278. NeurIPS 2024Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
279. NeurIPS 2024Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in LLMs
280. NeurIPS 2024Unchosen Experts Can Contribute Too: Unleashing MoE Models’ Power by Self-Contrast
281. NeurIPS 2024Understanding Emergent Abilities of Language Models from the Loss Perspective
282. NeurIPS 2024Understanding Transformers via N-Gram Statistics
283. NeurIPS 2024UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation
284. NeurIPS 2024Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought
285. NeurIPS 2024Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
286. NeurIPS 2024UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction
287. NeurIPS 2024Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
288. NeurIPS 2024Watermarking Makes Language Models Radioactive
289. NeurIPS 2024Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles
290. NeurIPS 2024Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
291. NeurIPS 2024What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration
292. NeurIPS 2024What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information
293. NeurIPS 2024WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
294. NeurIPS 2024WizardArena: Post-training Large Language Models via Simulated Offline Chatbot Arena
295. NeurIPS 2024Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads
296. NeurIPS 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
297. NeurIPS 2024Zero-Shot Tokenizer Transfer
298. NeurIPS 2024Zipfian Whitening

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。