🎓How I Study AIHISA
📖Read
📄Papers📰Blogs🎬Courses
💡Learn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
📝Daily Log🎯Prompts🧠Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Papers4

AllBeginnerIntermediateAdvanced
All SourcesarXiv
#Qwen

NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

Intermediate
Hyochan Chong, Dongkyu Kim et al.Feb 6arXiv

NanoQuant is a new way to shrink large language models down to 1-bit and even less than 1-bit per weight without retraining on huge datasets.

#post-training quantization#sub-1-bit quantization#binary LLMs

Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate Forgetting

Intermediate
Muxi Diao, Lele Yang et al.Jan 5arXiv

Supervised fine-tuning (SFT) often makes a model great at a new task but worse at its old skills; this paper explains a key reason why and how to fix it.

#Entropy-Adaptive Fine-Tuning#confident conflicts#token-level entropy

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

Intermediate
Yuqing Li, Jiangnan Li et al.Dec 19arXiv

Humans keep a big-picture memory (a “mindscape”) when reading long things; this paper teaches AI to do the same.

#Retrieval-Augmented Generation#Mindscape#Hierarchical Summarization

OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value

Intermediate
Mengzhang Cai, Xin Gao et al.Dec 16arXiv

OpenDataArena (ODA) is a fair, open platform that measures how valuable different post‑training datasets are for large language models by holding everything else constant.

#OpenDataArena#post-training datasets#data-centric AI