Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning wi
What should we make of OpenAI’s GPT-4, anyway? Is the large language model a major step on the way to an artificial general intelligence (AGI)—the insider’s term for an AI system with a flexible human-level intellect? And if we do create an AGI, might it be so different from human intelligence that it doesn’t see the point of keeping Homo sapiens around? If you query the world’s best minds on basi
本記事は、当社オウンドメディア「Doors」に移転しました。 約5秒後に自動的にリダイレクトします。 このたびブレインパッドは、LLM/Generative AIに関する研究プロジェクトを立ち上げ、この「Platinum Data Blog」を通じてLLM/Generative AIに関するさまざまな情報を発信をしています。 この記事では、GPT-4の登場から執筆日(2023年5月31日時点)までの2ヶ月間で登場した論文を振り返りながら、まとめて紹介していきます。 LLM/ChatGPTの動向 オープンソースLLM モデル オープンソースLLMの調整 Adapter、LoRA Instruction Tuning Human Feedback プロンプトエンジニアリング プロンプトエンジニアリングの課題①:プロンプトに大量の情報を入れられない プロンプトエンジニアリングの課題②:複雑なタス
Generative AI systems across modalities, ranging from text, image, audio, and video, have broad social impacts, but there exists no official standard for means of evaluating those impacts and which impacts should be evaluated. We move toward a standard approach in evaluating a generative AI system for any modality, in two overarching categories: what is able to be evaluated in a base system that h
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based mo
ResearchGPTs are GPTs: An early look at the labor market impact potential of large language models We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GP
We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifica
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstr
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