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Distilling Reasoning Without Knowledge: A Framework for Reliable LLMs
🤖AI Summary
Researchers propose a new framework for large language models that separates planning from factual retrieval to improve reliability in fact-seeking question answering. The modular approach uses a lightweight student planner trained via teacher-student learning to generate structured reasoning steps, showing improved accuracy and speed on challenging benchmarks.
Key Takeaways
- →A modular framework explicitly separates planning from factual retrieval and answer synthesis in LLMs.
- →The lightweight student planner is trained using only planning traces and fact requests, without factual answers or evidence.
- →Results on SEAL-0 benchmark show improved accuracy and latency compared to monolithic reasoning models.
- →The approach addresses inefficient tool usage issues in current retrieval-augmented LLMs.
- →Explicitly learned planning structures are demonstrated to be essential for reliable fact-seeking LLMs.
#large-language-models#llm-reasoning#fact-checking#retrieval-augmented-generation#ai-planning#machine-learning#arxiv-research#teacher-student-learning
Read Original →via arXiv – CS AI
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