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 9 / 33 页

401. ICML 2026Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning
402. ICML 2026Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
403. ICML 2026Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
404. ICML 2026Gram2Token: Enabling Run-time GPU-Native Grammar-Constrained Decoding for LLMs
405. ICML 2026Graph of States: Solving Abductive Tasks with Large Language Models
406. ICML 2026Graph-Link: Bridging the Semantic-Structural Gap in Text-to-SQL via Constrained Subgraph Induction
407. ICML 2026GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
408. ICML 2026Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
409. ICML 2026Group-wise Data Ordering: Enhancing Instruction Tuning of Large Language Models via Embedding Proximity
410. ICML 2026Grouter: Decoupling Routing from Representation for Accelerated MoE Training
411. ICML 2026GRPO is Secretly a Process Reward Model
412. ICML 2026GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache
413. ICML 2026Guidance: Sentence-Level Citation Enforcement via Prefix-Tail Guidance during LLM Decoding
414. ICML 2026Guided Star-Shaped Masked Diffusion
415. ICML 2026h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
416. ICML 2026Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment
417. ICML 2026Hallucination Detection from Structural Reasoning Model
418. ICML 2026Harnessing Non-Adversarial Robustness in Large Language Models
419. ICML 2026Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping
420. ICML 2026Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents
421. ICML 2026HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench
422. ICML 2026Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
423. ICML 2026HeraSys: Collaborative Serving of Multiple LLM Workflows via Fine-Grained End-to-End Optimization
424. ICML 2026Heterogeneous Customizable Personalized Federated Fine-Tuning Approach for Large Language Models
425. ICML 2026HiCI: Hierarchical Construction–Integration for Long-Context Attention
426. ICML 2026HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling
427. ICML 2026Hierarchical Abstract Tree for Cross-Document Retrieval Augmented Generation
428. ICML 2026HInT: Hypergraph Infusion at the Structural Layers Improves Table Understanding
429. ICML 2026HiPhO: How Far Are (M)LLMs from Humans in the Latest High School Physics Olympiad Benchmark?
430. ICML 2026Hista and Numca: Estimate State Value Effectively for Large Language Model Reinforcement Learning
431. ICML 2026How Do Language Models Speak Languages? A Case Study on Unintended Code-Switching
432. ICML 2026How do LLMs Compute Verbal Confidence?
433. ICML 2026How does Chain of Thought decompose complex tasks?
434. ICML 2026How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning
435. ICML 2026How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning
436. ICML 2026How Language Models Process Negation
437. ICML 2026How Powerful are LLMs in Generating Formal Program Specifications?
438. ICML 2026How Reasoning Evolves from Post-Training Data: An Empirical Study Using Chess
439. ICML 2026How to Correctly Report LLM-as-a-Judge Evaluations
440. ICML 2026How to Fine-Tune a Reasoning Model? A Teacher–Student Cooperation Framework to Synthesize Student-Consistent SFT Data
441. ICML 2026How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models
442. ICML 2026How Transformers Represent Hierarchies: A Local-to-Global Mechanism
443. ICML 2026How2Everything: Mining the Web for How-to Procedures to Evaluate and Improve LLMs
444. ICML 2026Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models
445. ICML 2026Identifying and Mitigating Errors in Gradient Aggregation of Distributed Data Parallel Training
446. ICML 2026IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research
447. ICML 2026Imagination Helps Visual Reasoning, But Not Yet in Latent Space
448. ICML 2026ImgCoT: Compressing Long Chain of Thought into Compact Visual Tokens for Efficient Reasoning of Large Language Model
449. ICML 2026Imitation Learning for Multi-turn LM Agents via On-policy Expert Corrections
450. ICML 2026Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR

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。