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LLMs enhance knowledge entity relation extraction in academic papers · 3 sources tracked

By PulseAugur Editorial · [3 sources] ·

A new research paper explores various strategies for improving knowledge entity relation extraction (KERE) in academic articles using large language models (LLMs). The study evaluates multiple approaches, including closed-source LLM prompting, LoRA-adapted open-source LLMs, and LLM-enhanced small language models (SLMs). Experimental results indicate that a LoRA-adapted T5 model achieved the highest performance, with macro F1 scores of 0.7408 on abstracts and 0.7013 on full-text datasets. The research also highlights that LLMs can help mitigate errors in SLMs, with varying effectiveness depending on the error type. AI

IMPACT This research could lead to more accurate and efficient extraction of information from academic literature, aiding researchers and knowledge discovery.

RANK_REASON This is a research paper detailing novel methods and experimental results for a specific NLP task.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs enhance knowledge entity relation extraction in academic papers · 3 sources tracked

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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Yingyi Zhang, Chengzhi Zhang ·

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

    arXiv:2610.41552v1 Announce Type: new Abstract: 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 …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chengzhi Zhang ·

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

    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 a…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chengzhi Zhang ·

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

    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 a…

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