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Selective State-Space Adaptation and Retrieval for Language Model Reasoning
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers propose MaLoRA and MaRA adapters that use state-space recurrence to improve language model reasoning, achieving up to 18.2% relative gains over standard LoRA on multi-hop reasoning tasks.
- 为何重要
- Engineers fine-tuning frozen language models for reasoning tasks should consider this when standard low-rank adaptation leaves accuracy gaps on complex question-answering benchmarks.
- 注意
- Paper is recent preprint with no reported code availability yet; gains tested only on three specific models and two reasoning benchmarks, generalization unclear.
收听本摘要
- language model
- reasoning
- retrieval
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