Post
1811
š Evaluating Long Context #1: Long Range Arena (LRA)
Accurately evaluating how well language models handle long contexts is crucial, but it's also quite challenging to do well. In this series of posts, we're going to examine the various benchmarks that were proposed to assess long context understanding, starting with Long Range Arens (LRA)
Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.
š Key Features of LRA
1ļøā£ Diverse Tasks: The LRA benchmark consists of a suite of tasks designed to evaluate model performance on long sequences ranging from 1,000 to 16,000 tokens. These tasks encompass different data types and modalities: Text, Natural and Synthetic Images, and Mathematical Expressions.
2ļøā£ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
3ļøā£ Open-Source and Extensible: Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
š Tasks
1ļøā£ Long ListOps
2ļøā£ Byte-level Text Classification and Document Retrieval
3ļøā£ Image Classification
4ļøā£ Pathfinder and Pathfinder-X (Long-range spatial dependency)
šØāš» Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena
š Long Range Arena (LRA) paper: Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)
Accurately evaluating how well language models handle long contexts is crucial, but it's also quite challenging to do well. In this series of posts, we're going to examine the various benchmarks that were proposed to assess long context understanding, starting with Long Range Arens (LRA)
Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.
š Key Features of LRA
1ļøā£ Diverse Tasks: The LRA benchmark consists of a suite of tasks designed to evaluate model performance on long sequences ranging from 1,000 to 16,000 tokens. These tasks encompass different data types and modalities: Text, Natural and Synthetic Images, and Mathematical Expressions.
2ļøā£ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
3ļøā£ Open-Source and Extensible: Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
š Tasks
1ļøā£ Long ListOps
2ļøā£ Byte-level Text Classification and Document Retrieval
3ļøā£ Image Classification
4ļøā£ Pathfinder and Pathfinder-X (Long-range spatial dependency)
šØāš» Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena
š Long Range Arena (LRA) paper: Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)
