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 31 / 33 页

1501. ICML 2025Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
1502. ICML 2025Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
1503. ICML 2025Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models
1504. ICML 2025TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation
1505. ICML 2025Towards Cost-Effective Reward Guided Text Generation
1506. ICML 2025Towards Lifelong Model Editing via Simulating Ideal Editor
1507. ICML 2025Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond
1508. ICML 2025Training a Generally Curious Agent
1509. ICML 2025Training Software Engineering Agents and Verifiers with SWE-Gym
1510. ICML 2025TruthFlow: Truthful LLM Generation via Representation Flow Correction
1511. ICML 2025TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs
1512. ICML 2025Tuning LLM Judge Design Decisions for 1/1000 of the Cost
1513. ICML 2025UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning
1514. ICML 2025Unbiased Evaluation of Large Language Models from a Causal Perspective
1515. ICML 2025Understanding Bias Reinforcement in LLM Agents Debate
1516. ICML 2025Understanding Chain-of-Thought in LLMs through Information Theory
1517. ICML 2025Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
1518. ICML 2025Understanding Synthetic Context Extension via Retrieval Heads
1519. ICML 2025Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism
1520. ICML 2025Unlocking Post-hoc Dataset Inference with Synthetic Data
1521. ICML 2025Unnatural Languages Are Not Bugs but Features for LLMs
1522. ICML 2025VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
1523. ICML 2025VinePPO: Refining Credit Assignment in RL Training of LLMs
1524. ICML 2025Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
1525. ICML 2025What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning?
1526. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
1527. ICML 2025What Makes In-context Learning Effective for Mathematical Reasoning
1528. ICML 2025When Bad Data Leads to Good Models
1529. ICML 2025Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
1530. ICML 2025Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
1531. ICML 2025WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
1532. ICML 2025XAttention: Block Sparse Attention with Antidiagonal Scoring
1533. ICML 2025xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
1534. ICML 2025ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
1535. ICML 2025µnit Scaling: Simple and Scalable FP8 LLM Training
1536. ICML 2023A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
1537. ICML 2023Active Learning based Structural Inference
1538. ICML 2023Are Large Kernels Better Teachers than Transformers for ConvNets?
1539. ICML 2023Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
1540. ICML 2023Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
1541. ICML 2023Bag of Tricks for Training Data Extraction from Language Models
1542. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
1543. ICML 2023Bidirectional Learning for Offline Model-based Biological Sequence Design
1544. ICML 2023BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models
1545. ICML 2023Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
1546. ICML 2023CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
1547. ICML 2023CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
1548. ICML 2023Compositional Exemplars for In-context Learning
1549. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
1550. ICML 2023ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction

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。