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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Status: Unchecked ×Keyword: GPT-2 ×Clear all

3 claims from 2 papers

1. Computer Science › Topic Modeling

   LoRA Fine-Tuning of a 3B Code LLM for Algorithmic Efficiency

   Hu, Shen, Wallis et al. · arXiv (Cornell University) · 2021

   The paper proposes Low-Rank Adaptation (LoRA), which freezes pre-trained weights and trains small added matrices, sharply cutting trainable parameters and memory while keeping model quality.

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   1. UncheckedLoRA matches or beats full fine-tuning on RoBERTa, DeBERTa, GPT-2 and GPT-3 with fewer trainable parameters and no added inference delay.“LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency.”

2. Computer Science › Topic Modeling

   The Pile: An 800GB Dataset of Diverse Text for Language Modeling

   Gao, Biderman, Black et al. · arXiv (Cornell University) · 2020

   The paper presents the Pile, an 825 GiB English text corpus built from 22 diverse subsets for training large language models, and evaluates models on it and its components.

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   1. UncheckedWithout further tuning, GPT-2 and GPT-3 perform poorly on many parts of the Pile, including academic writing, according to the paper's evaluation.“Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing.”
   2. UncheckedModels trained on the Pile score significantly better than those trained on Raw CC or CC-100 across all Pile components, and also improve on downstream tests.“Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations.”