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LLMs benchmarked on biomedical and NLP relation extraction tasks · 2 sources tracked

By PulseAugur Editorial · [3 sources] ·

A new paper benchmarks large language models (LLMs) on biomedical relation extraction, finding that proprietary models like OpenAI O1 and Gemini 2.0 Pro achieve state-of-the-art results. The study utilized the SNPPhenA corpus for tasks including sentence-level, abstract-level, and association strength classification. While OpenAI O1 excelled in few-shot sentence-level classification, Gemini 2.0 Pro performed best on the challenging association strength task. Another paper introduces SciNLP, a new benchmark dataset for full-text entity and relation extraction specifically within the Natural Language Processing (NLP) domain, aiming to facilitate more comprehensive analysis of scientific literature. AI

IMPACT Highlights the increasing capability of LLMs in specialized scientific domains and the need for domain-specific benchmarks.

RANK_REASON Two academic papers introducing new datasets and benchmarking LLMs on specific NLP tasks.

Read on arXiv cs.CL →

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

LLMs benchmarked on biomedical and NLP relation extraction tasks · 2 sources tracked

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Two academic papers introducing new datasets and benchmarking LLMs on specific NLP tasks.
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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Claudiu Creanga, Liviu P. Dinu, Daniela Gifu ·

    Fine-Tuning Models for Biomedical Relation Extraction

    arXiv:2609.20169v1 Announce Type: new Abstract: Next-Generation Sequencing has revolutionized the study of genetic mutations, enabling large-scale investigations into their roles in disease development. However, extracting meaningful insights from the vast amount of biomedical li…

  2. arXiv cs.CL TIER_1 English(EN) · Claudiu Creanga, Teodor Marchitan, Liviu P. Dinu ·

    Benchmarking Large Language Models for Biomedical Relation Extraction

    arXiv:2609.19071v1 Announce Type: new Abstract: Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid architectures, and state-of-the-art LLMs (Gemini 2.0, OpenAI O-series, Qwen, Mistra…

  3. arXiv cs.CL TIER_1 English(EN) · Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang ·

    SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP

    arXiv:2509.07801v5 Announce Type: replace Abstract: Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While existing datasets aid model development, most focus on specific publication sec…

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