用大模型生成数据优化招聘候选者排序，显著提升精准度。
Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data
- 通过大模型合成多样正负样本，重塑语义空间
- 在300个职位描述上实现Recall@50提升至77.55%
- 无需人工标注，适合大规模招聘系统部署
招聘中的候选人筛选可视为两阶段检索与重排流程，以召回率为首要目标且受审查预算限制。上游检索器返回每个职位描述（JD）的候选人初筛列表，本文目标是重排该列表，使合格候选人尽可能靠前。我们提出mira-embeddings-v1，一种针对招聘领域的语义重排系统，通过大模型合成训练数据重构嵌入空间，并使用轻量级边界感知重排头修正边界混淆问题。从真实JD出发，构建五阶段提示管道，生成多样化正样本与难负样本，多角度塑造语义空间。随后进行两轮LoRA微调：先在JD间进行对比学习，再在异构文本数据集上进行JD-CV三元组对齐。关键优势在于无需大规模人工标注工业级训练对，仅需少量真实JD即可通过大模型合成扩展监督信号。最后引入BoundaryHead MLP对Top-K结果进行重排，以区分同标题但职责范围不同的岗位。在本地300个真实JD池中，相比基线模型（Recall@50: 68.89%），mira-embeddings-v1提升至77.55%，Precision@10由35.77%增至39.62%。在支持性全球池（44,138名候选人）上，采用Qwen3-32B评分标准，Recall@200达0.7047，优于基线的0.5969。结果表明，结合大模型合成监督与边界感知重排，可在无重型交叉编码器前提下实现稳健性能提升。
原文摘要 · Abstract (English)
Candidate sourcing for recruiters is best viewed as a two-stage retrieval and reranking pipeline with recall as the primary objective under a limited review budget. An upstream production retriever first returns a candidate shortlist for each job description (JD), and our goal is to rerank that shortlist so that qualified candidates appear as high as possible. We present mira-embeddings-v1, a semantic reranking system for the recruitment domain that reshapes the embedding space with LLM-synthesized training data and corrects boundary confusions with a lightweight reranking head. Starting from real JDs, we build a five-stage prompt pipeline to generate diverse positive and hard negative samples that sculpt the semantic space from multiple angles. We then apply a two-round LoRA adaptation: JD--JD contrastive training followed by JD--CV triplet alignment on a heterogeneous text dataset. Importantly, these gains require no large-scale manually labeled industrial training pairs: a modest set of real JDs is expanded into supervision through LLM synthesis. Finally, a BoundaryHead MLP reranks the Top-K results to distinguish between roles that share the same title but differ in scope. On a local pool of 300 real JDs with candidates from an upstream production retriever, mira-embeddings-v1 improves Recall@50 from 68.89% (baseline) to 77.55% while lifting Precision@10 from 35.77% to 39.62%. On a supportive global pool over 44,138 candidates judged by a Qwen3-32B rubric, it achieves Recall@200 of 0.7047 versus 0.5969 for the baseline. These results show that LLM-synthesized supervision with boundary-aware reranking yields robust gains without a heavy cross-encoder.
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