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

713 papers, page 13 of 15

601. NeurIPS 2023QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
602. NeurIPS 2023RADAR: Robust AI-Text Detection via Adversarial Learning
603. NeurIPS 2023RaLEs: a Benchmark for Radiology Language Evaluations
604. NeurIPS 2023Random-Access Infinite Context Length for Transformers
605. NeurIPS 2023RealTime QA: What's the Answer Right Now?
606. NeurIPS 2023REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
607. NeurIPS 2023Regularizing Neural Networks with Meta-Learning Generative Models
608. NeurIPS 2023RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
609. NeurIPS 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests
610. NeurIPS 2023Reproducibility study of the Fairness-enhanced Node Representation Learning
611. NeurIPS 2023ResMem: Learn what you can and memorize the rest
612. NeurIPS 2023Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
613. NeurIPS 2023Rethinking Gauss-Newton for learning over-parameterized models
614. NeurIPS 2023Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale
615. NeurIPS 2023Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
616. NeurIPS 2023REx: Data-Free Residual Quantization Error Expansion
617. NeurIPS 2023RoboCLIP: One Demonstration is Enough to Learn Robot Policies
618. NeurIPS 2023Robust Learning with Progressive Data Expansion Against Spurious Correlation
619. NeurIPS 2023RRHF: Rank Responses to Align Language Models with Human Feedback
620. NeurIPS 2023S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
621. NeurIPS 2023SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
622. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
623. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
624. NeurIPS 2023Scaling laws for language encoding models in fMRI
625. NeurIPS 2023Scaling MLPs: A Tale of Inductive Bias
626. NeurIPS 2023Scaling Riemannian Diffusion Models
627. NeurIPS 2023Schema-learning and rebinding as mechanisms of in-context learning and emergence
628. NeurIPS 2023Scientific Document Retrieval using Multi-level Aspect-based Queries
629. NeurIPS 2023Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time
630. NeurIPS 2023Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
631. NeurIPS 2023Self-Chained Image-Language Model for Video Localization and Question Answering
632. NeurIPS 2023Self-Evaluation Guided Beam Search for Reasoning
633. NeurIPS 2023Semantic HELM: A Human-Readable Memory for Reinforcement Learning
634. NeurIPS 2023Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
635. NeurIPS 2023Semi-Implicit Denoising Diffusion Models (SIDDMs)
636. NeurIPS 2023Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
637. NeurIPS 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
638. NeurIPS 2023Skill-it! A data-driven skills framework for understanding and training language models
639. NeurIPS 2023Smooth, exact rotational symmetrization for deep learning on point clouds
640. NeurIPS 2023Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
641. NeurIPS 2023SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
642. NeurIPS 2023SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
643. NeurIPS 2023Sparse Deep Learning for Time Series Data: Theory and Applications
644. NeurIPS 2023Sparse Parameterization for Epitomic Dataset Distillation
645. NeurIPS 2023SpecTr: Fast Speculative Decoding via Optimal Transport
646. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations
647. NeurIPS 2023Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
648. NeurIPS 2023Structural Pruning for Diffusion Models
649. NeurIPS 2023Structured Prediction with Stronger Consistency Guarantees
650. NeurIPS 2023Structured State Space Models for In-Context Reinforcement Learning

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.