The Unique Taste of LLMs for Papers: Potential issues in Using LLMs for Digital Library Document Recommendation Tasks
<|reference_start|>The Unique Taste of LLMs for Papers: Potential issues in Using LLMs for Digital Library Document Recommendation Tasks: This paper investigates the performance of several representative large models in the field of literature recommendation and explores potential biases. The results indicate that while some large models' recommendations can be somewhat satisfactory after simple manual screening, overall, the accuracy of these models in specific literature recommendation tasks is generally moderate. Additionally, the models tend to recommend literature that is timely, collaborative, and expands or deepens the field. In scholar recommendation tasks. There is no evidence to suggest that LLMs exacerbate inequalities related to gender, race, or the level of development of countries.<|reference_end|>
@article{tian2024the,
    title={The Unique Taste of LLMs for Papers: Potential issues in Using LLMs for
  Digital Library Document Recommendation Tasks},
    author={Yifan Tian, Yixin Liu, Yi Bu},
    journal={arXiv preprint arXiv:2409.19868},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19868},
    primaryClass={cs.DL}
}