Scaling Laws for Neural Language Models

Target entity: Scaling Laws for Neural Language Models
Context triple: [Tom Henighan, coAuthorOf, Scaling Laws for Neural Language Models]

• A. Exploring the Limits of Language Modeling
  "Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
• B. Language Models are Unsupervised Multitask Learners
  "Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
• C. Extensions of recurrent neural network language model
  "Extensions of Recurrent Neural Network Language Model" is a research work by Tomas Mikolov that advances neural language modeling by improving and extending recurrent neural network architectures for better performance in natural language processing tasks.
• D. OPT: Open Pre-trained Transformer Language Models
  OPT: Open Pre-trained Transformer Language Models is a family of openly released large-scale transformer-based language models developed by Meta AI to provide transparent, reproducible alternatives to proprietary models like GPT-3.
• E. Embeddings from Language Models
  Embeddings from Language Models (ELMo) is a deep contextual word representation technique that uses bidirectional language models to capture rich, context-dependent meanings of words for natural language processing tasks.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Scaling Laws for Neural Language Models
Target entity description: "Scaling Laws for Neural Language Models" is a seminal research paper that empirically characterizes how the performance, data requirements, and computational costs of large language models predictably improve as model size and training resources increase.

• A. Exploring the Limits of Language Modeling
  "Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
• B. Language Models are Unsupervised Multitask Learners
  "Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
• C. Extensions of recurrent neural network language model
  "Extensions of Recurrent Neural Network Language Model" is a research work by Tomas Mikolov that advances neural language modeling by improving and extending recurrent neural network architectures for better performance in natural language processing tasks.
• D. OPT: Open Pre-trained Transformer Language Models
  OPT: Open Pre-trained Transformer Language Models is a family of openly released large-scale transformer-based language models developed by Meta AI to provide transparent, reproducible alternatives to proprietary models like GPT-3.
• E. Embeddings from Language Models
  Embeddings from Language Models (ELMo) is a deep contextual word representation technique that uses bidirectional language models to capture rich, context-dependent meanings of words for natural language processing tasks.
• F. None of above. chosen