Yao Lu, Lei Wang, Ee-Peng Lim, Palakorn Achananuparp, Shan Dong, Hieu Mai
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Sarasarahhhhh/job_prediction | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| compute_em_and_semantic_accuracy | Ran | Sarasarahhhhh/job_prediction/eval/eval_metrics.py pointer only (licence: NONE) · get_code("301eb315c4ccf763") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 highquality 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2604.21204")
get_code_for_paper("2604.21204")
have("2604.21204")
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