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 417 篇，第 7 / 9 页

301. ICML 2025RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
302. ICML 2025RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts
303. ICML 2025Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment
304. ICML 2025Reflection-Window Decoding: Text Generation with Selective Refinement
305. ICML 2025Regress, Don't Guess: A Regression-like Loss on Number Tokens for Language Models
306. ICML 2025Reinforced Lifelong Editing for Language Models
307. ICML 2025Reliable and Efficient Amortized Model-based Evaluation
308. ICML 2025Residual Matrix Transformers: Scaling the Size of the Residual Stream
309. ICML 2025Resolving Lexical Bias in Model Editing
310. ICML 2025ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
311. ICML 2025Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
312. ICML 2025Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding
313. ICML 2025Rethinking Chain-of-Thought from the Perspective of Self-Training
314. ICML 2025Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning
315. ICML 2025Retraining-free Merging of Sparse MoE via Hierarchical Clustering
316. ICML 2025Retrieval-Augmented Perception: High-resolution Image Perception Meets Visual RAG
317. ICML 2025Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks
318. ICML 2025ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification
319. ICML 2025Revisiting Chain-of-Thought in Code Generation: Do Language Models Need to Learn Reasoning before Coding?
320. ICML 2025Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization
321. ICML 2025Reward Modeling with Ordinal Feedback: Wisdom of the Crowd
322. ICML 2025Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
323. ICML 2025Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs
324. ICML 2025Reward-Guided Speculative Decoding for Efficient LLM Reasoning
325. ICML 2025Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift
326. ICML 2025RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
327. ICML 2025RLTHF: Targeted Human Feedback for LLM Alignment
328. ICML 2025Robust Multimodal Large Language Models Against Modality Conflict
329. ICML 2025Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
330. ICML 2025ROPO: Robust Preference Optimization for Large Language Models
331. ICML 2025RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
332. ICML 2025RuleAdapter: Dynamic Rules for training Safety Reward Models in RLHF
333. ICML 2025RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization
334. ICML 2025Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
335. ICML 2025Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
336. ICML 2025Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
337. ICML 2025Scaling Inference-Efficient Language Models
338. ICML 2025Scaling Laws for Floating–Point Quantization Training
339. ICML 2025Scaling Laws for Upcycling Mixture-of-Experts Language Models
340. ICML 2025Scaling Sparse Feature Circuits For Studying In-Context Learning
341. ICML 2025Scaling Test-Time Compute Without Verification or RL is Suboptimal
342. ICML 2025Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI
343. ICML 2025SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
344. ICML 2025SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
345. ICML 2025ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference
346. ICML 2025SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning
347. ICML 2025SkipGPT: Each Token is One of a Kind
348. ICML 2025SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
349. ICML 2025SlimLLM: Accurate Structured Pruning for Large Language Models
350. ICML 2025SNS-Bench: Defining, Building, and Assessing Capabilities of Large Language Models in Social Networking Services

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。