Deep learning · large language models papers, 2023–2026

Key findings

• Deep learning · large language models went from 488 papers in 2023 to 1,131 in 2026, roughly 2.3x in three years.
• ICML publishes the most Deep learning · large language models work: 1,608 papers from 2023 to 2026, 69% of the four-venue total.
• The fastest-growing overlap is with AI Agents: papers tagged with both rose from 5 in 2023 to 184 in 2026.
• Among the 11 topics under Deep Learning, it ranks #3 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 2.3x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Deep learning · large language models papers

488 papers, page 8 of 10

351. NeurIPS 2023OpenAGI: When LLM Meets Domain Experts
352. NeurIPS 2023OpenAssistant Conversations - Democratizing Large Language Model Alignment
353. NeurIPS 2023Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
354. NeurIPS 2023Pairwise Causality Guided Transformers for Event Sequences
355. NeurIPS 2023PAPR: Proximity Attention Point Rendering
356. NeurIPS 2023Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
357. NeurIPS 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
358. NeurIPS 2023Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck
359. NeurIPS 2023Passive learning of active causal strategies in agents and language models
360. NeurIPS 2023Paxion: Patching Action Knowledge in Video-Language Foundation Models
361. NeurIPS 2023Pengi: An Audio Language Model for Audio Tasks
362. NeurIPS 2023Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
363. NeurIPS 2023PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
364. NeurIPS 2023PoET: A generative model of protein families as sequences-of-sequences
365. NeurIPS 2023PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
366. NeurIPS 2023Polyhedron Attention Module: Learning Adaptive-order Interactions
367. NeurIPS 2023Post Hoc Explanations of Language Models Can Improve Language Models
368. NeurIPS 2023Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
369. NeurIPS 2023Preference-grounded Token-level Guidance for Language Model Fine-tuning
370. NeurIPS 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
371. NeurIPS 2023Propagating Knowledge Updates to LMs Through Distillation
372. NeurIPS 2023ProPILE: Probing Privacy Leakage in Large Language Models
373. NeurIPS 2023ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
374. NeurIPS 2023QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Your Data
375. NeurIPS 2023Quantification of Uncertainty with Adversarial Models
376. NeurIPS 2023QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
377. NeurIPS 2023RADAR: Robust AI-Text Detection via Adversarial Learning
378. NeurIPS 2023RaLEs: a Benchmark for Radiology Language Evaluations
379. NeurIPS 2023Random-Access Infinite Context Length for Transformers
380. NeurIPS 2023RealTime QA: What's the Answer Right Now?
381. NeurIPS 2023REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
382. NeurIPS 2023Regularizing Neural Networks with Meta-Learning Generative Models
383. NeurIPS 2023RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
384. NeurIPS 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests
385. NeurIPS 2023Reproducibility study of the Fairness-enhanced Node Representation Learning
386. NeurIPS 2023ResMem: Learn what you can and memorize the rest
387. NeurIPS 2023Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
388. NeurIPS 2023Rethinking Gauss-Newton for learning over-parameterized models
389. NeurIPS 2023Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale
390. NeurIPS 2023Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
391. NeurIPS 2023REx: Data-Free Residual Quantization Error Expansion
392. NeurIPS 2023RoboCLIP: One Demonstration is Enough to Learn Robot Policies
393. NeurIPS 2023Robust Learning with Progressive Data Expansion Against Spurious Correlation
394. NeurIPS 2023RRHF: Rank Responses to Align Language Models with Human Feedback
395. NeurIPS 2023S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
396. NeurIPS 2023SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
397. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
398. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
399. NeurIPS 2023Scaling laws for language encoding models in fMRI
400. NeurIPS 2023Scaling MLPs: A Tale of Inductive Bias

FAQ

How many Deep learning · large language models papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

2,334 in total: 488 in 2023, 298 in 2024, 417 in 2025, 1,131 in 2026.

How many Deep learning · large language models papers are at NeurIPS 2024?

NeurIPS 2024 has 298 Deep learning · large language models papers, versus 415 the year before.

How many Deep learning · large language models papers are at ICLR 2025?

ICLR 2025 has 13 Deep learning · large language models papers.

How many Deep learning · large language models papers are at ICML 2026?

ICML 2026 has 1,131 Deep learning · large language models papers, versus 404 the year before.

Which conference publishes the most Deep learning · large language models papers?

ICML, with 1,608 papers from 2023 to 2026.