DQA：面向IT支持的诊断问题解答
DQA: Diagnostic Question Answering for IT Support
AI总结：
DQA通过维护持续诊断状态和聚合根本原因，提升企业IT支持中的系统性故障排查效率，其在150个匿名场景中取得78.7%的成功率，优于多轮RAG基线的41.3%。
摘要：
企业IT支持交互本质上是诊断性的：有效的解决需要从模糊用户报告中迭代收集证据以识别根本原因。虽然检索增强生成（RAG）通过历史案例提供支撑，但标准多轮RAG系统缺乏显式的诊断状态，因此在多轮中难以积累证据和解决竞争性假设。我们引入DQA，一种维护持续诊断状态并按根本原因聚合检索案例的诊断问题解答框架。DQA结合对话查询重写、检索聚合和状态条件响应生成，以支持企业在企业延迟和上下文限制下的系统性故障排查。我们在150个匿名企业IT支持场景中使用回放协议评估DQA。在三次独立运行的平均下，DQA在轨迹级成功标准下达到78.7%的成功率，相比多轮RAG基线的41.3%，同时将平均轮次从8.4降至3.9。
英文摘要：
Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retrieval-augmented generation (RAG) provides grounding through historical cases, standard multi-turn RAG systems lack explicit diagnostic state and therefore struggle to accumulate evidence and resolve competing hypotheses across turns. We introduce DQA, a diagnostic question-answering framework that maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. DQA combines conversational query rewriting, retrieval aggregation, and state-conditioned response generation to support systematic troubleshooting under enterprise latency and context constraints. We evaluate DQA on 150 anonymized enterprise IT support scenarios using a replay-based protocol. Averaged over three independent runs, DQA achieves a 78.7% success rate under a trajectory-level success criterion, compared to 41.3% for a multi-turn RAG baseline, while reducing average turns from 8.4 to 3.9.
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