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 1 / 6 页

1. NeurIPS 2024$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$
2. NeurIPS 2024$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning
3. NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
4. NeurIPS 2024A distributional simplicity bias in the learning dynamics of transformers
5. NeurIPS 2024A Full-duplex Speech Dialogue Scheme Based On Large Language Model
6. NeurIPS 2024A Gradient Accumulation Method for Dense Retriever under Memory Constraint
7. NeurIPS 2024A Polar coordinate system represents syntax in large language models
8. NeurIPS 2024A teacher-teacher framework for clinical language representation learning
9. NeurIPS 2024Accelerating Blockwise Parallel Language Models with Draft Refinement
10. NeurIPS 2024Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling
11. NeurIPS 2024Adaptable Logical Control for Large Language Models
12. NeurIPS 2024Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment
13. NeurIPS 2024Adversarial Moment-Matching Distillation of Large Language Models
14. NeurIPS 2024Agent Planning with World Knowledge Model
15. NeurIPS 2024AGILE: A Novel Reinforcement Learning Framework of LLM Agents
16. NeurIPS 2024AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
17. NeurIPS 2024Aligning LLM Agents by Learning Latent Preference from User Edits
18. NeurIPS 2024Aligning to Thousands of Preferences via System Message Generalization
19. NeurIPS 2024Alignment at Pre-training! Towards Native Alignment for Arabic LLMs
20. NeurIPS 2024Alignment for Honesty
21. NeurIPS 2024AlphaMath Almost Zero: Process Supervision without Process
22. NeurIPS 2024AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
23. NeurIPS 2024AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback
24. NeurIPS 2024An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding
25. NeurIPS 2024ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
26. NeurIPS 2024Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans
27. NeurIPS 2024AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
28. NeurIPS 2024AutoMix: Automatically Mixing Language Models
29. NeurIPS 2024AutoPSV: Automated Process-Supervised Verifier
30. NeurIPS 2024AutoSurvey: Large Language Models Can Automatically Write Surveys
31. NeurIPS 2024AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
32. NeurIPS 2024BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
33. NeurIPS 2024Base of RoPE Bounds Context Length
34. NeurIPS 2024BERTs are Generative In-Context Learners
35. NeurIPS 2024Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
36. NeurIPS 2024Block Transformer: Global-to-Local Language Modeling for Fast Inference
37. NeurIPS 2024Boosting the Potential of Large Language Models with an Intelligent Information Assistant
38. NeurIPS 2024Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
39. NeurIPS 2024Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
40. NeurIPS 2024Calibrating Reasoning in Language Models with Internal Consistency
41. NeurIPS 2024Can Language Models Learn to Skip Steps?
42. NeurIPS 2024Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
43. NeurIPS 2024Cascade Speculative Drafting for Even Faster LLM Inference
44. NeurIPS 2024CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
45. NeurIPS 2024Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
46. NeurIPS 2024Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
47. NeurIPS 2024Chain-of-Thought Reasoning Without Prompting
48. NeurIPS 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAG
49. NeurIPS 2024Co-occurrence is not Factual Association in Language Models
50. NeurIPS 2024Code Repair with LLMs gives an Exploration-Exploitation Tradeoff

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。