用轻量路由判断是否需要多步检索，省下大量算力。
RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering
- 基于一次检索结果的六个特征，设计无需额外调用大模型的决策路由。
- 在多个数据集上保持SOTA性能，仅消耗41%-49%的令牌开销。
- 适合预算紧张但需高精度的多跳问答场景。
多跳问答系统通常对每个问题进行昂贵的重复检索，通过多次重写或分解问题、搜索桥接实体来回答。这些策略依赖多次大模型调用，增加额外令牌成本，在大模型预算有限时并不合适。我们分析发现，许多多跳问题已可通过单次一击式RAG正确回答，因此对所有问题都执行额外检索会浪费预算。为此，我们提出RASER（可恢复性感知的可选升级路由器），一套基于单次一击式RAG和六种特征构建的廉价路由机制。RASER-2决定是否停止或升级至额外检索操作PRUNE；RASER-3在单次一击式RAG、PRUNE与迭代检索IRCoT之间选择，使用相同特征并引入显式的成本-准确率权衡。两个路由器均不产生额外大模型调用。在六个大模型和三个多跳问答基准上，二者在F1指标上均与现有最佳方法相当，同时仅消耗始终使用PRUNE策略时41%-49%的令牌，也低于迭代和分解检索基线。
原文摘要 · Abstract (English)
Multi-hop question-answering systems often use expensive retrieval on every question. They may decompose the question, run several retrieval rounds, or search through bridge entities before answering. All of these strategies rely on repeated LLM calls to rewrite or decompose the question, which increases extra token cost, and it is not fitting when the LLM budget is tight. However, our analysis shows that lots of multi-hop questions are already answered correctly by a single one-shot RAG, so running an extra retrieval on every question wastes the budget. We introduce RASER (Recoverability-Aware Selective Escalation Router), a family of cheap routers built on one-shot RAG and six features from it. RASER-2 decides whether to stop or escalate to the extra-retrieval action PRUNE. RASER-3 chooses among one-shot RAG, PRUNE, and iterative retrieval IRCoT, using the same features but adding an explicit cost-accuracy trade-off. Neither router makes an extra LLM call to decide. Across six LLMs and three multi-hop QA benchmarks, both routers stay competitive with the other state-of-the-art (SOTA) baselines in F1 while spending only 41-49% of always-prune's tokens and also less than the iterative and decomposition retrieval baselines.
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