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Optimization & Theory

6,829 papers (27 classified directly here)

Subtopics

Model Compression (1,785) Optimization (1,664) Efficient Computing (1,579) Theory (1,082) Neural Network Optimization (903) Evaluation (509) Loss Functions (123) Interpretability (105) Stochastic Methods (97) Regularization (77) Generalization (24) Sparse Optimization (7)

Papers per year

8
50
642
1173
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Papers (including subtopics)

BIG-Bench Extra Hard ACL 2025
InductionBench: LLMs Fail in the Simplest Complexity Class ACL 2025
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates ACL 2025
Scaling Laws and Efficient Inference for Ternary Language Models ACL 2025
“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization ACL 2025
StitchLLM: Serving LLMs, One Block at a Time ACL 2025
Better Embeddings with Coupled Adam ACL 2025
Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation ACL 2025
Benchmarking Long-Context Language Models on Long Code Understanding ACL 2025
A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability ACL 2025
LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models ACL 2025
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory ACL 2025
Information Locality as an Inductive Bias for Neural Language Models ACL 2025
Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning ACL 2025
Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking ACL 2025
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport ACL 2025
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport ACL 2025
Value Residual Learning ACL 2025
SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers ACL 2025
Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models ACL 2025
Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models ACL 2025
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory ACL 2025
Length-Induced Embedding Collapse in PLM-based Models ACL 2025
Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding ACL 2025
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization ACL 2025