LLM Reasoning in 2025, Violent Delights Have Violent Ends
LLM Reasoning in 2025.
LLM Reasoning in 2025.
The second post on my "some very-personal questions to myself" series. It's been over a year since last post and many progress on LLM have been made from academic/industry, which partially solves my questions. I will introduce these works and ask myself some new questions. see last post here[previous_post]. This post is about Pretrain Ceiling, Second Half, Scaling the Environment.
Some very-personal questions, assumptions and predictions on the future after the large model era. I hope to keep it a habit for writing such future-ask post for every half year to keep me thinking about the "next token" in the AI era. This post is about Compression, World Model, Agent and Alignment.
A simple note on the RL used in single-agent and multi-agent.
A brief review of the VC dimension. All discussions are based on the simple case of binary classification.
Read Dr. Bang Liu’s paper Natural Language Processing and Text Mining with Graph-Structured Representations from the University of Alberta and take some notes.
Study notes for Stanford CS224W: Machine Learning with Graphs by Jure Leskovec.
A brief note on the CLSciSumm Workshop that the CIST lab participated in, the main focus is on methods. The experiments are analysised in detail in papers. Papers:
Record the incremental decoding processing of parallel decoding models such as CNN seq2seq and Transformer in the inference phase in Fairseq.
reading note for STRUCTURED NEURAL SUMMARIZATION.
Long time no see, SVM.
Reading note for reformer.
Note for Hierarchical Latent Dirichlet Allocation
Record some recent processing of heterogeneous information networks
Graph-based Automatic Summary Related Paper Selection Reading
rl study note, minimalist style
Selected Reading of ACL/NAACL 2019 Automatic Summarization Papers
DPPs Similarity Measurement Improvement
STRASS: Backpropagation for Extractive Summarization
Translate first, then generate the abstract
Reading Comprehension + Automatic Abstract
BiSET: Retrieve + Fast Rerank + Selective Encoding + Template Based
Note for paper "Cognitive Graph for Multi-Hop Reading Comprehension at Scale."
Selected readings from ACL 2019 award-winning papers.
Variational Autoencoder Learning Notes
Reference Article:
On VAE, the original paper and the two blogs above have already explained it very clearly. I am just repeating and paraphrasing, just to go through it myself. If anyone reads this blog, I recommend reading these three reference sources first
Convolutional Sequence to Sequence
Robust Unsupervised Cross-Lingual Word Embedding Mapping
Course Notes on Computational Linguistics, Reference Textbook: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition.