GPT1

Improving Language Understanding by Generative Pre-Training

前言

  • 将之前词向量等无监督称之为Semi-supervised learning，将GPT1这种无监督称之为Unsupervised pre-training（作为Semi-supervised learning的子集）

Framework

Unsupervised pre-training

给定一个句子$U=(u_i)$，极大似然
$$
L_1(\mathcal{U})=\sum_i \log P\left(u_i \mid u_{i-k}, \ldots, u_{i-1} ; \Theta\right)
$$

$k$为上下文窗口大小

Supervised fine-tuning

$$
…
$$

$$
L_3(\mathcal{C})=L_2(\mathcal{C})+\lambda * L_1(\mathcal{C})
$$

Setup

BERT-base对标于GPT1

GPT1 BERT-base
参数量 110M 110M
数据集 BooksCorpus(800M words) BooksCorpus & English Wikipedia(2,500M words)
vocabulary BPE WordPiece
position embeddings 可学习 可学习
发布时间点 2018.06 2018.10

GPT2

创新点

  • zero-shot
  • prompt （使用该范式，但未明确定义该范式）

相比于GPT1

GPT1 GPT2
参数量 110M 1.5B
数据集 BooksCorpus(800M words) WebText(Reddit<40 GB)

WebText来自于Common Crawl（一个公开爬虫脚本）

GPT2各版本参数量

Parameters Layers $d_{model}$
117M 12 768
345M 24 1024
762M 36 1280
1542M 48 1600

GPT3

创新点

  • few-shot（非fine-tuning，无梯度计算）

Framework

meta-learning

in-context learning

Setup

training-data 训练了一个回归模型，去过滤WebText中的数据

参数量

InstructGPT & ChatGPT

We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup.

ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022.

GPT-3.5 series is a series of models that was trained on a blend of text and code from before Q4 2021.

Framework

通过OpenAI API获取到的一些prompts去微调了GPT-3(175B)，对模型返回的N个答案，让人进行排序并反馈给模型（RLHF），得到InstructGPT(1.3B)

作者认为当前主流LMs中，预测下一个词的预训练任务和prompt是存在差异的，然后提出了该方法：
  1. SFT——对收集到的质量好的prompt采用人工标注答案后，对GPT3.5进行微调（成本过大）
  2. 采用人工对模型返回的几种结果，进行排序来训练RM（在线学习耗时久，成本也不小）
  3. 得到RM后，通过RL来训练模型

通过把最后的softmax换成全连接映射为维度为1的标量当作RM的奖励gt。RM模型loss（Pairwise Rank Loss）
$$
\operatorname{loss}(\theta)=-\frac{1}{\left(\begin{array}{c}
K \
2
\end{array}\right)} E_{\left(x, y_w, y_l\right) \sim D}\left[\log \left(\sigma\left(r_0\left(x, y_w\right)-r_o\left(x, y_l\right)\right)\right)\right]
$$

整体优化目标函数
$$
\begin{aligned}
\operatorname{objective}(\phi)= & E_{(x, y) \sim D_{\pi_\phi^{\text {RL }}}}\left[r_\theta(x, y)-\beta \log \left(\pi_\phi^{\mathrm{RL}}(y \mid x) / \pi^{\mathrm{SFT}}(y \mid x)\right)\right]+ \
& \gamma E_{x \sim D_{\text {pretrain }}}\left[\log \left(\pi_\phi^{\mathrm{RL}}(x)\right)\right]
\end{aligned}
$$

Setup

The SFT dataset contains about 13k training prompts (from the API and labeler-written), the RM dataset has 33k training prompts (from the API and labeler-written), and the PPO dataset has 31k training prompts (only from the API).