In DeltaProduct (Siems et al., 2025), they propose to improve DeltaNet (Yang et al., 2025) by updating the online memory with $n_h$ KVs for each token, which can be seen as performing multiple steps of gradient descent per token. I will explain how this method is almost the same as multi-KV DeltaNet and reveal a potential flaw in the design of DeltaProduct.
2025
Implementating Test-Time Training - Part 1
This blog post is part 1 of a series that describes my attempt in implementing the Test-Time Training (TTT) model proposed by Sun et al. (2024), and Titans, proposed by Behrouz et al., (2024). At the time of writing, these two are two strong recurrent language models, but they have not yet open-sourced their implementation (TTT has only open-sourced the Jax implementation).
2024
(EREN) Robust and Scalable Model Editing for Large Language Models
TL;DR: A reader is augmented with a growing notebook that caches all edits in natural texts, and the reader retrieves relevant edits and make inference based on them. This achieves SOTA in model editing in QA and fact-checking.
InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens
The first benchmark for evaluating the effectiveness of LLMs in handling more than 100k tokens!
In the paper, we name it $\infty$-Bench, but I will sometimes use "InfiniteBench" in this blog post for better readability.
Finally got some time to write this blog, been so busy lately! I have been in a fairly long duration of research hiatus, meanwhile the field of NLP has been revolutionized by an overwhelming number of new LLMs. Finally, I was able to arrive at some productive and meaningful work in this new era of research, as a second author. In this blog post, I will introduce this work that I have been working on recently.
2023
Safety and Ethical Concerns of Large Language Models
I will be holding a seminar at ModelBest (面壁智能) in Sep 20, 2023 in Beijing, Haidian, 科技园. The seminar will be in Chinese, and it's called "大模型安全与伦理问题" (translation: Safety and Ethical Concerns of Large Language Models). Below is a list of references.
CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics
Code | Paper (on hold by ArXiv) | Paper (preprints.org) | 知乎
I did this work with my girlfriend, whose research direction is computational fluid dynamics (CFD). We observed that there are numerous research works in applying deep learning (DL) to solve CFD problems. E.g., Pangu-Weather have shown that DL methods can not only be more accurate than the best numerical methods, but can also be multiple magnitudes faster.