Addressing Representation Collapse in Vector Quantized Models with One Linear Layer
SimVQ, 坐标变换替代可学习 Codebook
Note
Restructuring Vector Quantization with The Rotation Trick
一种利用 Rotation Trick 来替代 STE 的方案
Let’s Verify Step by Step
来自 OpenAI 的 process supervision
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
来自 DeepSeek 的 process supervision
Universal Prompt Tuning for Graph Neural Networks
图上特征 prompt 等价各异 graph prompt
All in One: Multi-Task Prompting for Graph Neural Networks
统一 graph/edge/node-level 的 graph prompt
Base of RoPE Bounds Context Length
讨论 RoPE base 对于相似 Tokens 感知能力的影响
Round and Round We Go! What makes Rotary Positional Encodings useful?
理解 RoPE 的高低频
Transformers need glasses! Information Over-Squashing in Language Tasks
LLM Representational Collapse
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
TTA, Test-Time Augmentation