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Keyword: GPT-3 ×Clear all

7 claims from 5 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

   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.”

3. Social Sciences › Misinformation and Its Impacts

   TruthfulQA: Measuring How Models Mimic Human Falsehoods

   Lin, Hilton and Evans · arXiv (Cornell University) · 2021

   The authors built a benchmark of 817 questions to test whether language models give truthful answers, and found that models fell well short of humans and that the largest were generally the least truthful.

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   1. UncheckedOn the TruthfulQA benchmark, the best language model tested gave truthful answers to 58% of questions, against 94% for human performance.“The best model was truthful on 58% of questions, while human performance was 94%.”

4. Computer Science › Topic Modeling

   Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

   Wang, Xu, Lan et al. · arXiv (Cornell University) · 2023

   The paper proposes Plan-and-Solve prompting, which asks a language model to plan and then carry out subtasks, to fix errors in Zero-shot-CoT, and tests it on ten datasets across three reasoning problems.

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   1. UncheckedWith GPT-3, the authors' zero-shot Plan-and-Solve prompting beat Zero-shot-CoT on all datasets and matched 8-shot CoT on maths problems.“The experimental results over GPT-3 show that our proposed zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought Prompting, and has comparable performance with 8-shot CoT prompting on the math reaso…”

5. Computer Science › Topic Modeling

   Task Contamination: Language Models May Not Be Few-Shot Anymore

   Li and Flanigan · arXiv (Cornell University) · 2023

   The paper examines whether zero-shot and few-shot results of large language models are inflated by task contamination, tracking performance over time and using several methods to look for evidence of it.

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   1. UncheckedIn this study, language models did surprisingly better on datasets released before their training data was created than on later ones, after controlling for difficulty.“Utilizing GPT-3 series models and several other recent open-sourced LLMs, and controlling for dataset difficulty, we find that on datasets released before the LLM training data creation date, LLMs perform surprisingly better than on datasets released after.”