GitHub | Paper (upcoming)

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.

Code | Paper

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.

The blog post

I can't believe I haven't read this until now. This is mind-provoking, and the result is an important step towards understanding neural networks.

Paper

TLDR: Propose ActAdd, a method for controlling model behavior during inference by modifying activations with a bias term that is learned from a pair of prompt.

Summary:

• Propose ActAdd, a method for controlling model behavior by modifying activations at inference time.
• Steering vectors are computed by taking the activation differences that result from pairs of prompts. The vectors are added as bias during inference.
• ActAdd provides control over high-level properties of the output, and preserves off-target model performance, and requires little computational and implementational costs.

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.