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 17 / 47 页

801. ICML 2026Rethinking LLM Ensembling from the Perspective of Mixture Models
802. ICML 2026Rethinking Personalization in Large Language Models at the Token Level
803. ICML 2026Rethinking Pretraining Data Detection for LLMs: From Local to Global
804. ICML 2026Rethinking Sparse Mixture of Experts from a Unified Perspective
805. ICML 2026Rethinking the Trust Region in LLM Reinforcement Learning
806. ICML 2026Rethinking Thinking Tokens: LLMs as Improvement Operators
807. ICML 2026Retrieval-Aware Distillation for Transformer-SSM Hybrids
808. ICML 2026Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
809. ICML 2026Revealing Behavioral Plasticity in Large Language Models: A Token-Conditional Perspective
810. ICML 2026Revealing Long-context Potential of Attention Heads via Frequency Kernels
811. ICML 2026REVIS: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models
812. ICML 2026Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics
813. ICML 2026Revisiting Efficiency–Accuracy Scaling in Mixture-of-Experts Architectures
814. ICML 2026Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
815. ICML 2026Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios
816. ICML 2026Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective
817. ICML 2026Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
818. ICML 2026Reward-free Alignment for Conflicting Objectives
819. ICML 2026RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
820. ICML 2026RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
821. ICML 2026RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
822. ICML 2026Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models
823. ICML 2026RSPO: Regularized Self-Play Alignment of Large Language Models
824. ICML 2026Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Non-Verifiable Domains
825. ICML 2026RubricRobustness: Evaluating the Sensitivity of Rubrics-Based Benchmarks to Simple Perturbations
826. ICML 2026Rule2DRC: Benchmarking LLM Agents for DRC Script Synthesis with Execution-Guided Test Generation
827. ICML 2026S-Quant: Rethinking Weight Quantization with Seed-Based Generation
828. ICML 2026SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling
829. ICML 2026SafeCompass: Dynamic Chain-of-Thought Steering via Inference-Time Safety Signals
830. ICML 2026SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety
831. ICML 2026SafeSeek: Universal Attribution of Safety Circuits in Language Models
832. ICML 2026SafeSpec: Fast and Safe LLM via Dynamic Reflective Sampling
833. ICML 2026Safety Anchor: Defending Harmful Fine-tuning via Geometric Bottlenecks
834. ICML 2026Safety Recovery in Reasoning Models Is Only a Few Early Steering Steps Away
835. ICML 2026SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs
836. ICML 2026SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling
837. ICML 2026SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
838. ICML 2026Sampling from Your Language Model One Byte at a Time
839. ICML 2026SARSteer: Safeguarding Large Audio Language Models via Safe-Ablated Refusal Steering
840. ICML 2026ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training
841. ICML 2026Scaling Beyond Masked Diffusion Language Models
842. ICML 2026Scaling Generative Verifiers For Natural Language Mathematical Proof Verification And Selection
843. ICML 2026Scaling Inference-Time Computation via Opponent Simulation: Enabling Online Strategic Adaptation in Repeated Negotiation
844. ICML 2026Scaling Law for Quantization-Aware Training
845. ICML 2026Scaling Prompt Synthesis for Large Language Model Reasoning
846. ICML 2026Scaling-Aware Adapter for Structure-Grounded LLM Reasoning
847. ICML 2026ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning
848. ICML 2026Scene Graph Thinking: Reinforcing Structured Visual Reasoning for Multimodal Large Language Models
849. ICML 2026Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions
850. ICML 2026Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models

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。