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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering
TL;DR
IterCOMP is a novel framework for multi-hop question answering that compresses prompts by iteratively integrating essential evidence. It significantly improves accuracy and efficiency, outperforming existing methods.
✦ Why It Matters
Implement IterCOMP to enhance your multi-hop question answering systems today.
Key Takeaways
- Adopt IterCOMP for improved prompt compression in multi-hop QA tasks.
- Utilize targeted follow-up questions to enhance evidence integration.
- Focus on reducing token usage while maintaining accuracy.
- Experiment with IterCOMP on various datasets to validate performance gains.
How It Works
IterCOMP operates by decomposing documents into smaller evidence segments and evaluating the answerability of questions. It generates follow-up questions that help in iteratively gathering essential evidence, which leads to a more compact and reasoning-focused prompt.
This iterative process allows the system to adaptively refine the prompt based on the complexity of the reasoning required.