In this work, we develop a novel reasoning approach to enhance the performance of large language models (LLMs) in future occupation prediction. In this approach, a reason generator first derives a ``reason'' for a user using his/her past education and career history. The reason summarizes the user's preference and is used as the input of an occupation predictor to recommend the user's next occupation. This two-step occupation prediction approach is, however, non-trivial as LLMs are not aligned with career paths or the unobserved reasons behind each occupation decision. We therefore propose to fine-tune LLMs improving their reasoning and occupation prediction performance. We first derive high-quality oracle reasons, as measured by factuality, coherence and utility criteria, using a LLM-as-a-Judge. These oracle reasons are then used to fine-tune small LLMs to perform reason generation and next occupation prediction. Our extensive experiments show that: (a) our approach effectively enhances LLM's accuracy in next occupation prediction making them comparable to fully supervised methods and outperforming unsupervised methods; (b) a single LLM fine-tuned to perform reason generation and occupation prediction outperforms two LLMs fine-tuned to perform the tasks separately; and (c) the next occupation prediction accuracy depends on the quality of generated reasons. Our code is available at https://github.com/Sarasarahhhhh/job_prediction.

翻译：在这项工作中，我们开发了一种新颖的推理方法，以增强大型语言模型（LLM）在未来职业预测中的性能。在该方法中，推理生成器首先利用用户过去的教育和职业历程，为其推导出一个“理由”。该理由总结了用户的偏好，并作为职业预测器的输入，用于推荐用户的下一个职业。然而，这种两步式的职业预测方法并非易事，因为LLM与职业路径或每个职业决策背后未被观察到的理由并不对齐。因此，我们提出对LLM进行微调，以提升其推理和职业预测性能。我们首先利用LLM作为评判者（LLM-as-a-Judge），根据真实性、连贯性和实用性标准，推导出高质量的理想理由。然后，使用这些理想理由对小型LLM进行微调，以执行理由生成和下个职业预测任务。我们的广泛实验表明：(a) 我们的方法有效提升了LLM在下个职业预测中的准确性，使其与全监督方法相媲美，并优于无监督方法；(b) 对单个LLM进行微调以同时执行理由生成和职业预测，其性能优于分别对两个LLM进行微调以单独执行这两项任务；(c) 下个职业预测的准确性取决于所生成理由的质量。我们的代码可在 https://github.com/Sarasarahhhhh/job_prediction 获取。