论文索引

A Novel Pulse-Agile Waveform Design Based on Random FM Waveforms for Range Sidelobe Suppression and Range Ambiguity Mitigation

Fast Inference from Transformers via Speculative Decoding

Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

FlashHead: Efficient Drop-In Replacement for the Classification Head in Language Model Inference

Language Agents: Foundations, Prospects, and Risks

MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens

PagedAttention

ReAct: Synergizing Reasoning and Acting in Language Models

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Self-Distillation for Multi-Token Prediction

TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference

Toolformer: Language Models Can Teach Themselves to Use Tools

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Demystifying When Pruning Works via Representation Hierarchies

Language Models are Few-Shot Learners

Learning Representations by Backpropagating Errors

Let's Verify Step by Step

On the difficulty of training Recurrent Neural Networks

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Model Parameters

Self-Consistency Improves Chain of Thought Reasoning in Language Models

Adaptive MLP Pruning for Large Vision Transformers

Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks

Bielik-Minitron-7B: Compressing Large Language Models via Structured Pruning and Knowledge Distillation for the Polish Language

Deterministic Differentiable Structured Pruning for Large Language Models

Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score

HiAP

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

Rényi Entropy: A New Token Pruning Metric for Vision Transformers

ResPrune: Text-Conditioned Subspace Reconstruction for Visual Token Pruning in Large Vision-Language Models

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

VLA-IAP: Training-Free Visual Token Pruning via Interaction Alignment for Vision-Language-Action Models

Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach

Big2Small: A Unifying Neural Network Framework for Model Compression

BinaryAttention

Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

LLVQ

LoRA: Low-Rank Adaptation of Large Language Models

Parameter-Efficient Transfer Learning for NLP

Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression

RAMP: Reinforcement Adaptive Mixed-Precision Quantization for Efficient On-Device LLM Inference

SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization

Anatomical Heterogeneity in Transformer Language Models

Grow, Assess, Compress: Adaptive Backbone Scaling for Memory-Efficient Class Incremental Learning

Grow, Don't Overwrite: Fine-tuning Without Forgetting

Growing Networks with Autonomous Pruning

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

AlpacaEval: An Automatic Evaluator for Instruction-Following Language Models

Challenges and Opportunities in NLP Benchmarking

Holistic Evaluation of Language Models

Measuring Massive Multitask Language Understanding

Attention Is All You Need

Attention Residuals

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Contextual Word Representations: A Contextual Introduction

Image Transformer

Language Models are Unsupervised Multitask Learners

Layer Normalization

The Illustrated BERT, ELMo, and co.

The Illustrated Transformer

The Llama 3 Herd of Models

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

Scaling Instruction-Finetuned Language Models

Training language models to follow instructions with human feedback

Distributed Representations of Words and Phrases and their Compositionality

Efficient Estimation of Word Representations in Vector Space

GloVe: Global Vectors for Word Representation

Improving Distributional Similarity with Lessons Learned from Word Embeddings