AI助手帮求职者优化陌生人推荐请求，提升成功率。
Building AI Agents to Improve Job Referral Requests to Strangers
- 用AI改写推荐请求，弱请求获显著提升。
- 引入RAG后，弱请求成功率提高14%且强请求不降。
- 适合想低成本测试沟通策略的求职者或研究者。
本文开发了AI代理，帮助求职者在专业在线社区中撰写更有效的职位推荐请求。核心流程包括一个改写代理（improver agent）和一个评估代理（evaluator agent），后者使用训练好的模型预测用户获得推荐的概率。大语言模型（LLM）对较弱的请求改写可提升其预测成功率，但可能降低较强的请求；通过引入检索增强生成（RAG）技术，能避免对强请求的负面影响，并进一步放大对弱请求的改进。实验表明，结合RAG的LLM改写使弱请求的预测成功率平均提升14%，且不影响强请求表现。尽管模型预测的成功率不能保证真实世界中的推荐数量，但为后续高成本真人实验提供了低成本的有效信号。
原文摘要 · Abstract (English)
This paper develops AI agents that help job seekers write effective requests for job referrals in a professional online community. The basic workflow consists of an improver agent that rewrites the referral request and an evaluator agent that measures the quality of revisions using a model trained to predict the probability of receiving referrals from other users. Revisions suggested by the LLM (large language model) increase predicted success rates for weaker requests while reducing them for stronger requests. Enhancing the LLM with Retrieval-Augmented Generation (RAG) prevents edits that worsen stronger requests while it amplifies improvements for weaker requests. Overall, using LLM revisions with RAG increases the predicted success rate for weaker requests by 14\% without degrading performance on stronger requests. Although improvements in model-predicted success do not guarantee more referrals in the real world, they provide low-cost signals for promising features before running higher-stakes experiments on real users.
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