LLM-Powered Ensemble Learning for Paper Source Tracing: A GPU-Free Approach
<|reference_start|>LLM-Powered Ensemble Learning for Paper Source Tracing: A GPU-Free Approach: We participated in the KDD CUP 2024 paper source tracing competition and achieved the 3rd place. This competition tasked participants with identifying the reference sources (i.e., ref-sources, as referred to by the organizers of the competition) of given academic papers. Unlike most teams that addressed this challenge by fine-tuning pre-trained neural language models such as BERT or ChatGLM, our primary approach utilized closed-source large language models (LLMs). With recent advancements in LLM technology, closed-source LLMs have demonstrated the capability to tackle complex reasoning tasks in zero-shot or few-shot scenarios. Consequently, in the absence of GPUs, we employed closed-source LLMs to directly generate predicted reference sources from the provided papers. We further refined these predictions through ensemble learning. Notably, our method was the only one among the award-winning approaches that did not require the use of GPUs for model training. Code available at https://github.com/Cklwanfifa/KDDCUP2024-PST.<|reference_end|>
@article{chen2024llm-powered,
    title={LLM-Powered Ensemble Learning for Paper Source Tracing: A GPU-Free
  Approach},
    author={Kunlong Chen, Junjun Wang, Zhaoqun Chen, Kunjin Chen, Yitian Chen},
    journal={arXiv preprint arXiv:2409.09383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09383},
    primaryClass={cs.LG cs.AI cs.CL}
}