ChatGPTがデジタル広告の業界構造をつくり替えつつある。大手各社は、ChatGPTを生かして開発したAI(人工知能)システムをバナー広告の制作工程に導入。広告制作の生産性向上に成果を上げている。広告制作に携わる人員構成の見直しや、顧客企業から受け取る報酬の体系にメスを入れる動きも始まった。 デジタル広告の中でもChatGPTの影響をもろに受けているのがキャッチコピーの文言をつくる作業だ。商材の種類や想定する閲覧者の属性といった情報を入力すると、瞬く間にキャッチコピーの文言が自動生成される。 ChatGPTをはじめとする生成AIをデジタル広告制作に積極的に活用している1社が、デジタル広告最大手のサイバーエージェントだ。同社は自社開発のデジタル広告制作支援システム「極予測AI」を使い、新たにつくったバナー広告の内容をAIが解析して広告効果の予測値を算出。既に配信しているバナー広告のうち広告
Azure OpenAI Service lets you tailor our models to your personal datasets using a process known as fine-tuning. This customization step will let you get more out of the service by providing: Higher quality results than what you can get just from prompt design The ability to train on more examples than can fit into a prompt Lower-latency requests A customized model improves on the few-shot learning
"A fantasy graph illustrating a chain of stars in a dark night with blue sky, digital art, super resolution". Midjourney V5 By Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, Tushar Khot, Wenhu Chen From University of Edinburgh, University of Washington, Allen Institute for AI, University of Waterloo [paper] [blog] [twitter] Recently, there are a lot of progress in LLMs. Many claim that a small
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervis
はじめに ここ数日間に日本語で学習させたLLMがいくつかでましたが、定量的に性能評価したい!ということで日本語LLMベンチマークライブラリのJGLUEを回してみました。 ついでにloraに対応してなかったのでlora用のコードに修正。 環境 AWS Ec2 p4dn.24xlage Deep Learning AMI GPU PyTorch 2.0.0 (Amazon Linux 2) 20230406 セットアップ 適当なディレクトリを作成してJGLUEとtransformerをclone mkdir benchmark cd benchmark git clone https://github.com/yahoojapan/JGLUE.git git clone https://github.com/huggingface/transformers.git -b v4.9.2 tran
Given the massive cost of language model pre-training, a non-trivial improvement of the optimization algorithm would lead to a material reduction on the time and cost of training. Adam and its variants have been state-of-the-art for years, and more sophisticated second-order (Hessian-based) optimizers often incur too much per-step overhead. In this paper, we propose Sophia, Second-order Clipped St
LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility. Our LLM.int8 blogpost showed how the techniques in the LLM.int8 paper were integrated in transformers using the bitsandbytes library. As we strive to make models even more accessible to anyone, we decided to collaborate with bitsandbytes again to allow users to run models
GPTCache: A Library for Creating Semantic Cache for LLM Queries Gorilla: An API store for LLMs LlamaHub: a library of data loaders for LLMs made by the community EVAL: Elastic Versatile Agent with Langchain. will execute all your requests. Auto-evaluator: a lightweight evaluation tool for question-answering using Langchain Langchain visualizer: visualization and debugging tool for LangChain workfl
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