🔗 Evaluating Long Context #1: Long Range Arena (LRA)
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🔗 Evaluating Long Context #1: Long Range Arena (LRA)
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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)
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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)
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Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.
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Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.
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📌 Key Features of LRA
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📌 Key Features of LRA
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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.
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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.
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2️⃣ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
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2️⃣ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
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3️⃣ Open-Source and Extensible:  Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
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3️⃣ Open-Source and Extensible:  Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
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📌 Tasks
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📌 Tasks
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1️⃣ Long ListOps
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1️⃣ Long ListOps
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2️⃣ Byte-level Text Classification and Document Retrieval
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2️⃣ Byte-level Text Classification and Document Retrieval
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3️⃣ Image Classification
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3️⃣ Image Classification
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4️⃣ Pathfinder and Pathfinder-X (Long-range spatial dependency)
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4️⃣ Pathfinder and Pathfinder-X (Long-range spatial dependency)
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👨‍💻 Long Range Arena (LRA) Github Repository: 
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👨‍💻 Long Range Arena (LRA) Github Repository: 
https://github.com/google-research/long-range-arena
https://github.com/google-research/long-range-arena
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📄 Long Range Arena (LRA) paper: 
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📄 Long Range Arena (LRA) paper: 
Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)
https://huggingface.co/papers/2011.04006
2011.04006
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https://huggingface.co/papers/2011.04006
🔗 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: https://huggingface.co/papers/2011.04006
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