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 2,334 篇，第 13 / 47 页

601. ICML 2026Milestone-Guided Policy Learning for Long-Horizon Language Agents
602. ICML 2026Mind the Gap: Catching Hallucinations via Evidence Drop on the Reasoning Manifold
603. ICML 2026Mind the Gap: Structure-Aware Consistency in Preference Learning
604. ICML 2026MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models
605. ICML 2026MineDraft: A Framework for Batch Parallel Speculative Decoding
606. ICML 2026Mining Tensor/Neuron-Level Sparsity to Maximize Mixture-of-Experts Potential in Post-Training and Inference
607. ICML 2026Mitigating Conversational Inertia in Multi-Turn Agents
608. ICML 2026Mitigating Error Propagation in Low-Rank Approximation of Large Models via Distribution-Aware Whitening
609. ICML 2026Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding
610. ICML 2026Mitigating Reward Hacking in LLM-based Recommendation: A Preference Optimization Approach
611. ICML 2026Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck
612. ICML 2026MixReasoning: Switching Modes to Think
613. ICML 2026MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
614. ICML 2026MMKU-Bench: A Multimodal Update Benchmark for Diverse Visual Knowledge
615. ICML 2026MOC: Multi-Order Communication in LLM-based Multi-Agent Systems
616. ICML 2026Modality-Decoupled Online Recursive Editing
617. ICML 2026Model Merging Scaling Laws in Large Language Models
618. ICML 2026Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models
619. ICML 2026Modeling Hierarchical Thinking in Large Reasoning Models
620. ICML 2026Modelling Attention with Aitchison Geometry: Token Distinguishability and Temperature Scaling
621. ICML 2026Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
622. ICML 2026MoLoRA: Composable Specialization via Per-Token Adapter Routing
623. ICML 2026Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoning
624. ICML 2026MonoScale: Scaling Multi-Agent System with Monotonic Improvement
625. ICML 2026More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing
626. ICML 2026MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
627. ICML 2026MRPO: Magnitude-Regularized Policy Optimization via L1 Constraints
628. ICML 2026Multi-Agent Teams Hold Experts Back
629. ICML 2026Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
630. ICML 2026Multi-Head LatentMoE and Head Parallel: Communication-Efficient and Deterministic MoE Parallelism
631. ICML 2026Multilingual Safety Alignment via Representation-Space Separability
632. ICML 2026Multilingual Safety Alignment Via Sparse Weight Editing
633. ICML 2026Multimodal Meta-Verifier with Explicit Structured Recalibration
634. ICML 2026Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining
635. ICML 2026NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models
636. ICML 2026NanoSpec: Accelerating Speculative Decoding using Minimalist In-Context Vocabularies
637. ICML 2026NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs
638. ICML 2026Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
639. ICML 2026Navigating the Pareto Frontier of Alignment: Spectrum-Adaptive Fine-Tuning for LLMs
640. ICML 2026NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
641. ICML 2026NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs
642. ICML 2026Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
643. ICML 2026NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels
644. ICML 2026NITP: Next Implicit Token Prediction for LLM Pre-training
645. ICML 2026NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
646. ICML 2026No More, No Less: Least-Privilege Language Models
647. ICML 2026Non-Monotonic Autoregressive Sequence Model
648. ICML 2026Non-Parametric Structural Priors for Geometry Theorem Prediction
649. ICML 2026Normalized Rewards for Preference Optimization
650. ICML 2026NorMuon: Making Muon more efficient and scalable

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。