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🔗 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.
📌 Key Features of LRA
1️⃣ 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.
2️⃣ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
3️⃣ Open-Source and Extensible: Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
📌 Tasks
1️⃣ Long ListOps
2️⃣ Byte-level Text Classification and Document Retrieval
3️⃣ Image Classification
4️⃣ Pathfinder and Pathfinder-X (Long-range spatial dependency)
👨💻 Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena
📄 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.
📌 Key Features of LRA
1️⃣ 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.
2️⃣ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
3️⃣ Open-Source and Extensible: Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
📌 Tasks
1️⃣ Long ListOps
2️⃣ Byte-level Text Classification and Document Retrieval
3️⃣ Image Classification
4️⃣ Pathfinder and Pathfinder-X (Long-range spatial dependency)
👨💻 Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena
📄 Long Range Arena (LRA) paper: Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)