GateKD is a confidence-gated closed-loop distillation framework that improves multi-step reasoning transfer from LLMs to smaller models by dynamically filtering supervision based on teacher reliability.
arXiv preprint arXiv:2410.09037 , year=
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InSemRAG combines dynamic intent-aware hybrid retrieval and semantics-preserving chunk repair in an iterative loop, yielding 2.65 F1 gain on HotPotQA and 1.5 accuracy gain on FEVER with 4.32x lower latency than Multi-Hop RAG via SLMs.
citing papers explorer
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GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning
GateKD is a confidence-gated closed-loop distillation framework that improves multi-step reasoning transfer from LLMs to smaller models by dynamically filtering supervision based on teacher reliability.
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Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
InSemRAG combines dynamic intent-aware hybrid retrieval and semantics-preserving chunk repair in an iterative loop, yielding 2.65 F1 gain on HotPotQA and 1.5 accuracy gain on FEVER with 4.32x lower latency than Multi-Hop RAG via SLMs.