4 Matching Annotations
  1. Oct 2023
  2. arxiv.org arxiv.org
    2305.15486.pdf
    2
    1. mark.crowley 25 Oct 2023

      Wu, Prabhumoye, Yeon Min, Bisk, Salakhutdinov, Azaria, Mitchell and Li. "SPRING: GPT-4 Out-performs RL Algorithms byStudying Papers and Reasoning". Arxiv preprint arXiv:2305.15486v2, May, 2023.

      reinforcement-learning nlp large-language-models chatgpt minecraft evaluation-methods rdgrp-f23
    2. mark.crowley 25 Oct 2023
      Quantitatively, SPRING with GPT-4 outperforms all state-of-the-art RLbaselines, trained for 1M steps, without any training.

      Them's fighten' words!

      I haven't read it yet, but we're putting it on the list for this fall's reading group. Seriously, a strong result with a very strong implied claim. they are careful to say it's from their empirical results, very worth a look. I suspect that amount of implicit knowledge in the papers, text and DAG are helping to do this.

      The Big Question: is their comparison to RL baselines fair, are they being trained from scratch? What does a fair comparison of any from-scratch model (RL or supervised) mean when compared to an LLM approach (or any approach using a foundation model), when that model is not really from scratch.

      reinforcement-learning rdgrp-f23 reading_group_crowley nlp larg deep-learning self-supervised supervised-learning evaluation-methods
    Visit annotations in context

    Tags

    Annotators

    URL

    arxiv.org/pdf/2305.15486.pdf
  3. Jun 2023
  4. d4mucfpksywv.cloudfront.net d4mucfpksywv.cloudfront.net
    Language Models are Unsupervised Multitask Learners
    2
    1. mark.crowley 28 Jun 2023
      13.19%

      that's a lot!

      evaluation-methods
    2. mark.crowley 28 Jun 2023
      The Bloom filterswere constructed such that the false positive rate is upperbounded by 1108 . We further verified the low false positiverate by generating 1M strings, of which zero were found bythe filter

      Bloom filters used to determine how much overlap there is between train and test set, to be more sure of their results.

      evaluation-methods
    Visit annotations in context

    Tags

    Annotators

    URL

    d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf