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

2,334 papers, page 36 of 47

1751. NeurIPS 2024Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure
1752. NeurIPS 2024Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
1753. NeurIPS 2024Preference Learning Algorithms Do Not Learn Preference Rankings
1754. NeurIPS 2024Probing the Decision Boundaries of In-context Learning in Large Language Models
1755. NeurIPS 2024Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
1756. NeurIPS 2024Protecting Your LLMs with Information Bottleneck
1757. NeurIPS 2024PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
1758. NeurIPS 2024QBB: Quantization with Binary Bases for LLMs
1759. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
1760. NeurIPS 2024Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing
1761. NeurIPS 2024QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
1762. NeurIPS 2024QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
1763. NeurIPS 2024Questioning the Survey Responses of Large Language Models
1764. NeurIPS 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
1765. NeurIPS 2024Reasons and Solutions for the Decline in Model Performance after Editing
1766. NeurIPS 2024Recursive Introspection: Teaching Language Model Agents How to Self-Improve
1767. NeurIPS 2024Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models
1768. NeurIPS 2024Reflective Multi-Agent Collaboration based on Large Language Models
1769. NeurIPS 2024ReFT: Representation Finetuning for Language Models
1770. NeurIPS 2024Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs
1771. NeurIPS 2024Representation Noising: A Defence Mechanism Against Harmful Finetuning
1772. NeurIPS 2024Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
1773. NeurIPS 2024Reranking Laws for Language Generation: A Communication-Theoretic Perspective
1774. NeurIPS 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search
1775. NeurIPS 2024Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
1776. NeurIPS 2024RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
1777. NeurIPS 2024RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models
1778. NeurIPS 2024Rule Based Rewards for Language Model Safety
1779. NeurIPS 2024SafeWorld: Geo-Diverse Safety Alignment
1780. NeurIPS 2024SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain
1781. NeurIPS 2024Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
1782. NeurIPS 2024Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
1783. NeurIPS 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
1784. NeurIPS 2024Scaling Sign Language Translation
1785. NeurIPS 2024SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge
1786. NeurIPS 2024Segmenting Watermarked Texts From Language Models
1787. NeurIPS 2024SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
1788. NeurIPS 2024SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
1789. NeurIPS 2024Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
1790. NeurIPS 2024Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
1791. NeurIPS 2024Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
1792. NeurIPS 2024Semantics and Spatiality of Emergent Communication
1793. NeurIPS 2024Sequoia: Scalable and Robust Speculative Decoding
1794. NeurIPS 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
1795. NeurIPS 2024SimPO: Simple Preference Optimization with a Reference-Free Reward
1796. NeurIPS 2024SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models
1797. NeurIPS 2024SlimGPT: Layer-wise Structured Pruning for Large Language Models
1798. NeurIPS 2024SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining
1799. NeurIPS 2024Smoothie: Label Free Language Model Routing
1800. NeurIPS 2024SnapKV: LLM Knows What You are Looking for Before Generation

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.