Language Model Benchmarks

Language models have become an integral part of the AI landscape, and benchmarking their performance is crucial for understanding their capabilities and limitations. This page provides an overview of the key benchmarks used to evaluate language models.

Key Benchmarks

1. GLUE (General Language Understanding Evaluation): A suite of tasks designed to evaluate the general language understanding capabilities of language models.
2. SuperGLUE: An extension of GLUE that includes more complex tasks and a wider range of language models.
3. BERT Score: A metric that measures the similarity between the predictions of a language model and the ground truth.
4. ROUGE: A metric used to evaluate the quality of text summarization.
5. BLEU: A metric used to evaluate the similarity between two sequences of text.

Language Models

• BERT: A pre-trained language representation model that has been widely used in various NLP tasks.
• GPT-3: A large language model developed by OpenAI, capable of generating human-like text.
• T5: A transformer-based model designed for tasks involving text-to-text transformations.

Resources

For more information on language model benchmarks, you can visit the following resources:

• GLUE Benchmark
• SuperGLUE Benchmark
• BERT Score
• ROUGE
• BLEU

BERT Architecture

GPT-3 Architecture

T5 Architecture