社会层面 · accountability transparency and interpretability论文趋势 2023–2026

主要结论

• 社会层面 · accountability transparency and interpretability论文从 2023 年的 301 篇增长到 2026 年的 1,357 篇，三年间扩大到约 4.5 倍。
• NeurIPS 是发社会层面 · accountability transparency and interpretability论文最多的会议，2023–2026 年共 1,275 篇，占四个会议总量的 37%。
• 交叉方向里涨得最快的是社会层面 · privacy：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 93 篇。
• 在「对齐与负责任 AI」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的社会层面 · safety是 93 倍，社会层面 · accountability transparency and interpretability是 4.5 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

• NeurIPS 2026A Unifying Perspective on Language Model Interpretability
• ICML 2026$\texttt{PRISM}$:A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
• ICLR 2026Automated Interpretability Metrics Do Not Distinguish Trained and Random Transformers
• CVPR 2026Beyond Matching to Tiles Bridging Unaligned Aerial and Satellite Views
• NeurIPS 2025$\mathcal{X}^2$-DFD: A framework for e$\mathcal{X}$plainable and e$\mathcal{X}$tendable Deepfake Detection
• ICML 2025(How) Can Transformers Predict Pseudo-Random Numbers?
• ICLR 2025Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers
• CVPR 2025Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
• NeurIPS 20242D-OOB: Attributing Data Contribution Through Joint Valuation Framework
• ICML 2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective
• ICLR 2024"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
• ICLR 2023Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
• CVPR 2023Analyzing and Diagnosing Pose Estimation With Attributions
• NeurIPS 2026AdKnob: Ad Intensity Control and Labeling for LLM-Native Advertising
• ICML 2026$\texttt{ShaplEIG}$: Bayesian Experimental Design for Shapley Value Estimation
• ICLR 2026Bandits with Single-Peaked Preferences and Limited Resources
• CVPR 2026IEBGLAn Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and

全部社会层面 · accountability transparency and interpretability论文

共 1,124 篇，第 19 / 23 页

901. ICML 2024Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better
902. ICML 2024Compressing Large Language Models by Joint Sparsification and Quantization
903. ICML 2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
904. ICML 2024Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
905. ICML 2024Contextual Feature Selection with Conditional Stochastic Gates
906. ICML 2024Counterfactual Metarules for Local and Global Recourse
907. ICML 2024Criterion Collapse and Loss Distribution Control
908. ICML 2024Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization
909. ICML 2024Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation
910. ICML 2024Detecting and Identifying Selection Structure in Sequential Data
911. ICML 2024Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution
912. ICML 2024DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
913. ICML 2024Does Label Smoothing Help Deep Partial Label Learning?
914. ICML 2024Don't trust your eyes: on the (un)reliability of feature visualizations
915. ICML 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
916. ICML 2024EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
917. ICML 2024Enhancing Implicit Shape Generators Using Topological Regularizations
918. ICML 2024Equivariant Frames and the Impossibility of Continuous Canonicalization
919. ICML 2024Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
920. ICML 2024Expand-and-Cluster: Parameter Recovery of Neural Networks
921. ICML 2024Exploration and Anti-Exploration with Distributional Random Network Distillation
922. ICML 2024Explorations of Self-Repair in Language Models
923. ICML 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
924. ICML 2024FESSNC: Fast Exponentially Stable and Safe Neural Controller
925. ICML 2024From Neurons to Neutrons: A Case Study in Interpretability
926. ICML 2024GNNs Also Deserve Editing, and They Need It More Than Once
927. ICML 2024Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
928. ICML 2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
929. ICML 2024Grokking Group Multiplication with Cosets
930. ICML 2024High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
931. ICML 2024High-Performance Temporal Reversible Spiking Neural Networks with $\mathcal{O}(L)$ Training Memory and $\mathcal{O}(1)$ Inference Cost
932. ICML 2024How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
933. ICML 2024How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?
934. ICML 2024How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
935. ICML 2024Imitation Learning from Purified Demonstrations
936. ICML 2024Improving Neural Additive Models with Bayesian Principles
937. ICML 2024Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
938. ICML 2024InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
939. ICML 2024Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications in Binary Classification
940. ICML 2024Interpretability Illusions in the Generalization of Simplified Models
941. ICML 2024Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
942. ICML 2024Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
943. ICML 2024KISA: A Unified Keyframe Identifier and Skill Annotator for Long-Horizon Robotics Demonstrations
944. ICML 2024Language Models Represent Beliefs of Self and Others
945. ICML 2024Large Language Models are Geographically Biased
946. ICML 2024LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
947. ICML 2024Learning and Forgetting Unsafe Examples in Large Language Models
948. ICML 2024Learning to Intervene on Concept Bottlenecks
949. ICML 2024Light and Optimal Schrödinger Bridge Matching
950. ICML 2024LLaGA: Large Language and Graph Assistant

常见问题

2023–2026 年四大会议一共有多少篇社会层面 · accountability transparency and interpretability论文？

共 3,411 篇，按年份分别是 2023 年 301 篇、2024 年 589 篇、2025 年 1,164 篇、2026 年 1,357 篇。

NeurIPS 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

NeurIPS 2026 有 416 篇社会层面 · accountability transparency and interpretability论文，上一届是 522 篇。

ICLR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICLR 2026 有 292 篇社会层面 · accountability transparency and interpretability论文，上一届是 211 篇。

ICML 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICML 2026 有 541 篇社会层面 · accountability transparency and interpretability论文，上一届是 337 篇。

CVPR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

CVPR 2026 有 108 篇社会层面 · accountability transparency and interpretability论文，上一届是 94 篇。

哪个会议的社会层面 · accountability transparency and interpretability论文最多？

NeurIPS，2023–2026 年共 1,275 篇。