Computer Science > Machine Learning
[Submitted on 14 Sep 2024 (v1), last revised 17 Sep 2024 (this version, v2)]
Title:LLM-Powered Ensemble Learning for Paper Source Tracing: A GPU-Free Approach
View PDF HTML (experimental)Abstract: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 this https URL.
Submission history
From: Kunjin Chen [view email][v1] Sat, 14 Sep 2024 09:21:46 UTC (619 KB)
[v2] Tue, 17 Sep 2024 01:35:25 UTC (507 KB)
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