Agentic Long Context Understanding QA
Dataset dedicated to understanding and answering questions about very long textual contexts. Optimized for SFT and DPO fine-tuning on LLM models.
Description
The dataset Agentic Long Context Understanding QA contains examples of questions and answers based on very long textual contexts, requiring models capable of processing and inferring over extended sequences. It is designed to allow supervised (SFT) and differentiable policy (DPO) training of language models, with a focus on advanced architectures such as ring-attention and DeepSpeed to optimize the management of long sequences.
What is this dataset for?
- Train models capable of managing very long contexts to improve QA understanding.
- Test and improve specialized attention techniques (ring-attention) over long sequences.
- Train models via SFT or DPO for complex tasks requiring extensive contextual memory.
Can it be enriched or improved?
Yes, the dataset can be enriched by adding new examples from specific or custom contexts, as well as by additional annotation to detail the types of questions or the difficulty of the contexts. The generation pipeline is open-source, making it easy to create extensions adapted to specific use cases.
🔎 In summary
🧠 Recommended for
- Advanced NLP researchers
- LLM developers
- QA projects on long documents
🔧 Compatible tools
- OpenRLHF
- DeepSpeed
- PyTorch frameworks
- Ring-attention libraries
💡 Tip
Use the generation pipeline provided to easily adapt the dataset to your specific needs by modifying the scripts.
Frequently Asked Questions
What type of models can you train with this dataset?
Mainly broad language models (LLM) capable of managing very long contexts, using specialized attention mechanisms.
Is this dataset suitable for NLP beginners?
No, it requires advanced technical skills to manage build pipelines and optimized models.
Can you enrich the dataset with your own data?
Yes, the open-source pipeline allows you to add custom examples and adapt the build scripts according to specific needs.
