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 1,131 篇，第 11 / 23 页

501. ICML 2026LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs
502. ICML 2026LAVA: A Unified Framework for Finetuning Language and Vision Models
503. ICML 2026Layer-wise Gradient Disentanglement: Decoupling Semantics and Preferences in Direct Preference Optimization
504. ICML 2026LazyAttention: Efficient Retrieval-Augmented Generation with Deferred Positional Encoding
505. ICML 2026LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
506. ICML 2026Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding
507. ICML 2026LEAP: Zone-Aware MCTS for LLM Self-Speculative Decoding
508. ICML 2026Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
509. ICML 2026Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-gated LLM
510. ICML 2026Learnability-Informed Fine-Tuning of Diffusion Language Models
511. ICML 2026Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs
512. ICML 2026Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
513. ICML 2026Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning
514. ICML 2026Learning More from Less: Unlocking Internal Representations for Benchmark Compression
515. ICML 2026Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
516. ICML 2026Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning
517. ICML 2026Learning Self-Correction in Vision–Language Models via Rollout Augmentation
518. ICML 2026Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs
519. ICML 2026Learning syntax without semantics: Disentangled tiny language models
520. ICML 2026Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models
521. ICML 2026Learning to Discover at Test Time
522. ICML 2026Learning to Evict from Key-Value Cache
523. ICML 2026Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
524. ICML 2026Learning to Memorize with Attributive and Associative Memory for Online Test-Time Adaptation of Vision-Language Models
525. ICML 2026Learning to Reason for Factuality
526. ICML 2026Learning to Route Languages for Multilingual Policy Optimization
527. ICML 2026Learning to Self-Verify Makes Language Models Better Reasoners
528. ICML 2026Learning When to Attend: Conditional Memory Access for Long-Context LLMs
529. ICML 2026LEGO-FL: Learning Heterogeneous Federated Models as a LEGO Assembly Games
530. ICML 2026Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
531. ICML 2026Less Is More in Federated Continual Learning: RieSelect for Conflict-Aware Layer Selection in LLMs
532. ICML 2026Less Is More: Elevating RAG via Performance-Driven Context Compression
533. ICML 2026Less Is More: Fast and Accurate Reasoning with Cross-Head Unified Sparse Attention
534. ICML 2026Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure
535. ICML 2026Leveraging Evidence Priors for Robust Prompt Learning under Noisy Supervision in Vision-Language Models
536. ICML 2026LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs
537. ICML 2026LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
538. ICML 2026Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks
539. ICML 2026LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
540. ICML 2026LightWM: Training-Free Hierarchical Working Memory for Small Language Model Agents
541. ICML 2026LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
542. ICML 2026LipoPU: Pocket-level Prediction of Lipid-Protein Interactions via Positive-Unlabeled Learning
543. ICML 2026LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
544. ICML 2026LiveNewsBench: Evaluating Web Search Agents with Freshly Curated News
545. ICML 2026LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
546. ICML 2026LLM-MatLogic: Executable Exchange Contracts for Knowledge-Graph Query Answering with Scoped Negation
547. ICML 2026LLMInertia: Adaptive Counter-Inertial Reasoning to Improve Evidence Faithfulness in Large Language Models
548. ICML 2026LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
549. ICML 2026LLMs Lean on Priors, Not Programming Language Semantics
550. ICML 2026Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control

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。