Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets
📰 ArXiv cs.AI
Learn to apply structured reasoning for scalable question answering over long document sets using LLMs, overcoming context window limitations
Action Steps
- Decompose long documents into chunks to avoid exceeding LLM context windows
- Apply structured reasoning to assemble answers from chunk-level outputs
- Implement aggregation methods to combine outputs from multiple chunks
- Test and evaluate the performance of the system on large document sets
- Optimize the system by fine-tuning LLMs and adjusting aggregation parameters
Who Needs to Know This
NLP engineers and researchers can benefit from this approach to improve question answering systems, especially when dealing with large document collections
Key Insight
💡 Structured reasoning can help scale question answering over long document sets by aggregating chunk-level outputs
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🤖 Overcome LLM context limits for question answering with structured reasoning and chunking! 📄
Key Takeaways
Learn to apply structured reasoning for scalable question answering over long document sets using LLMs, overcoming context window limitations
Full Article
Title: Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets
Abstract:
arXiv:2604.22294v1 Announce Type: cross Abstract: Real-world document question answering is challenging. Analysts must synthesize evidence across multiple documents and different parts of each document. However, any fixed LLM context window can be exceeded as document collections grow. A common workaround is to decompose documents into chunks and assemble answers from chunk-level outputs, but this introduces an aggregation bottleneck: as the number of chunks grows, systems must still combine and
Abstract:
arXiv:2604.22294v1 Announce Type: cross Abstract: Real-world document question answering is challenging. Analysts must synthesize evidence across multiple documents and different parts of each document. However, any fixed LLM context window can be exceeded as document collections grow. A common workaround is to decompose documents into chunks and assemble answers from chunk-level outputs, but this introduces an aggregation bottleneck: as the number of chunks grows, systems must still combine and
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