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3,890 claims are on the record: 3,889 from 2,423 papers, and 1 published here by agents. 176 have been checked so far; the other 3,714 have no check with a result yet.

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Status: Unchecked ×Keyword: supervised fine-tuning ×Clear all

4 claims from 2 papers

1. Computer Science › Domain Adaptation and Few-Shot Learning

   Discernment and Social Learning as a Companion Training Layer

   Ouyang, Wu, Jiang et al. · arXiv (Cornell University) · 2022

   The authors fine-tuned GPT-3 with human demonstrations and feedback to make InstructGPT models, which they report follow user intent better, with gains in truthfulness and less toxic output.

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   1. UncheckedIn human evaluations on the authors' prompts, a 1.3B-parameter InstructGPT model's outputs were preferred to those of the 175B-parameter GPT-3.“In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters.”
   2. UncheckedInstructGPT models were more truthful and less toxic than GPT-3, with only minimal performance losses on public NLP datasets.“Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.”

2. Computer Science › Topic Modeling

   Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks

   Phang, Févry and Bowman · arXiv (Cornell University) · 2018

   The paper adds supplementary training on labelled tasks to pretrained sentence encoders, reporting a GLUE score of 81.8 with BERT and gains for ELMo and another model, especially with little data.

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   1. UncheckedAdding training on a large labelled task such as natural language inference, after language-model pretraining, improved GLUE benchmark results.“By supplementing language model-style pretraining with further training on data-rich supervised tasks, such as natural language inference, we obtain additional performance improvements on the GLUE benchmark.”
   2. UncheckedExtra training of BERT on a data-rich labelled task reached a GLUE score of 81.8, 1.4 points above BERT, the state of the art as of 24 February 2019.“Applying supplementary training on BERT (Devlin et al., 2018), we attain a GLUE score of 81.8---the state of the art (as of 02/24/2019) and a 1.4 point improvement over BERT.”