BERT を中心に解説した資料です.BERT に比べると,XLNet と RoBERTa の内容は詳細に追ってないです.
あと,自作の図は上から下ですが,引っ張ってきた図は下から上になっているので注意してください.
もし間違い等あったら修正するので,言ってください.
(特に,RoBERTa の英語を読み間違えがちょっと怖いです.言い訳すいません.)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
This document summarizes a research paper on scaling laws for neural language models. Some key findings of the paper include:
- Language model performance depends strongly on model scale and weakly on model shape. With enough compute and data, performance scales as a power law of parameters, compute, and data.
- Overfitting is universal, with penalties depending on the ratio of parameters to data.
- Large models have higher sample efficiency and can reach the same performance levels with less optimization steps and data points.
- The paper motivated subsequent work by OpenAI on applying scaling laws to other domains like computer vision and developing increasingly large language models like GPT-3.
BERT を中心に解説した資料です.BERT に比べると,XLNet と RoBERTa の内容は詳細に追ってないです.
あと,自作の図は上から下ですが,引っ張ってきた図は下から上になっているので注意してください.
もし間違い等あったら修正するので,言ってください.
(特に,RoBERTa の英語を読み間違えがちょっと怖いです.言い訳すいません.)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
This document summarizes a research paper on scaling laws for neural language models. Some key findings of the paper include:
- Language model performance depends strongly on model scale and weakly on model shape. With enough compute and data, performance scales as a power law of parameters, compute, and data.
- Overfitting is universal, with penalties depending on the ratio of parameters to data.
- Large models have higher sample efficiency and can reach the same performance levels with less optimization steps and data points.
- The paper motivated subsequent work by OpenAI on applying scaling laws to other domains like computer vision and developing increasingly large language models like GPT-3.
This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.
This document summarizes recent research on applying self-attention mechanisms from Transformers to domains other than language, such as computer vision. It discusses models that use self-attention for images, including ViT, DeiT, and T2T, which apply Transformers to divided image patches. It also covers more general attention modules like the Perceiver that aims to be domain-agnostic. Finally, it discusses work on transferring pretrained language Transformers to other modalities through frozen weights, showing they can function as universal computation engines.
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.
This document summarizes recent research on applying self-attention mechanisms from Transformers to domains other than language, such as computer vision. It discusses models that use self-attention for images, including ViT, DeiT, and T2T, which apply Transformers to divided image patches. It also covers more general attention modules like the Perceiver that aims to be domain-agnostic. Finally, it discusses work on transferring pretrained language Transformers to other modalities through frozen weights, showing they can function as universal computation engines.
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
26. GPT3 課題
• 文章生成、推論に関する課題
• 長い文章を生成すると、文法的には正しいが同じ意味の単語を繰り返したり、結論が矛盾した
文章を生成することがある
• 人間の常識を持っていないため、差別的な表現などふさわしくない文章を生成することがある
• 意味不明な質問に対して「分からない」と言うことができない。
• Q: How many eyes does the sun have?(太陽の目はいくつありますか?))
• A: The sun has one eye.(太陽の目は1つです)
• 生成速度が遅い
• 1分間に150単語程しか生成できない
• 膨大な運用コスト
• モデルが巨大すぎてインフラ構築、事前学習にコストがかかる