Paper: arxiv.org/pdf/2410.05026

Joint work with the amazing @marbaga.bsky.social, @gmartius.bsky.social, @arkrause.bsky.social

We propose an algorithm that does this by actively maximizing expected information gain of the demonstrations, with a couple of tricks to estimate this quantity and mitigate forgetting.
Interestingly, this solution is viable even without any information about pre-training!

Our method significantly improves accuracy (measured as perplexity) for large language models and achieves a new state-of-the-art on the Pile benchmark.

If you're interested in test-time training or active learning, come chat with me at our poster session!

We introduce SIFT, a novel data selection algorithm for test-time training of language models. Unlike traditional nearest neighbor methods, SIFT uses uncertainty estimates to select maximally informative data, balancing relevance & diversity.

Paper: arxiv.org/pdf/2410.08020

Unfortunately not as of now. We may also release Jupyter notebooks in the future, but this may take some time.

I'm glad you find this resource useful Maximilian!

Noted. Thanks for the suggestion!

Very glad to hear that they’ve been useful to you! :)

table of contents:

Huge thanks to the countless people that helped in the process of bringing this resource together!

Preprint: arxiv.org/pdf/2410.08020

Paper: arxiv.org/pdf/2402.158...
Virtual poster: neurips.cc/virtual/2024...