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Enhancing Knowledge Entity Relation Extraction in Abstracts and Full-text Content of Academic Articles Using Large Language Models — arXiv:2610.41552

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41552
License (as archived): CC0-1.0
Archived: 2026-11-03T07:25:40.422Z
SHA-256 of the archived text: 749759dca21c002675897433a210df0163eaf253a7a1ad8efaabb138710f2c11
Stable URL of this page: https://metraipolis.com/documento/dc_665bbbbfd02c

How to cite

Enhancing Knowledge Entity Relation Extraction in Abstracts and Full-text Content of Academic Articles Using Large Language Models — arXiv:2610.41552 — arXiv (en), https://arxiv.org/abs/2610.41552 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_665bbbbfd02c (Metraipolis archive dc_665bbbbfd02c, sha256 749759dca21c002675897433a210df0163eaf253a7a1ad8efaabb138710f2c11)

BibTeX

@misc{dc665bbbbfd02c,
  title = {Enhancing Knowledge Entity Relation Extraction in Abstracts and Full-text Content of Academic Articles Using Large Language Models — arXiv:2610.41552},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41552},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_665bbbbfd02c; sha256 749759dca21c002675897433a210df0163eaf253a7a1ad8efaabb138710f2c11},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41552. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This study investigates the effectiveness of large pre-trained language models (LLMs) for knowledge entity relation extraction (KERE) from academic papers. Existing studies have examined individual types of LLM-based strategies and evaluated model performance using metrics such as precision and recall. To provide a broader evaluation, this study investigates multiple strategies, including closed-source LLM-based CoT prompting, LoRA-adapted open-source LLMs, LLM-enhanced small pre-trained language models (SLMs), and SLM-enhanced closed-source LLMs. In addition, this study introduces two strategies, namely SLM-enhanced open-source LLMs and closed-source LLM-enhanced open-source LLMs. These strategies are evaluated from multiple perspectives, including metrics, their ability to mitigate errors observed in SLMs, and potential directions for model optimization. This study manually constructs two annotated datasets in the NLP domain, covering Abstract and Full-Text datasets. Experimental results show that the LoRA-adapted T5 model achieves the highest macro F1 scores of 0.7408 and 0.7013 on the Abstract and Full-Text datasets, respectively. SLMs also benefit from LLM-generated entity descriptions under the LLM-enhanced SLM strategy. Error analysis further reveals that the effectiveness of LLM-based strategies in mitigating SLM errors varies across error types. Based on the identified limitations and error patterns of these strategies, several optimization directions are proposed. In addition, combining strategies with different error patterns further improves KERE performance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Digital Libraries (cs.DL); Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2610.41552 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:20:03.742Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).