A proof-of-concept multi-agent GPT system for microbial protein literature extraction shows both fine-tuning and prompt engineering improve cosine-similarity scores, with fine-tuning slightly ahead but more variable.
Summarization for Generative Relation Extraction in the Microbiome Domain
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We explore a generative relation extraction (RE) pipeline tailored to the study of interactions in the intestinal microbiome, a complex and low-resource biomedical domain. Our method leverages summarization with large language models (LLMs) to refine context before extracting relations via instruction-tuned generation. Preliminary results on a dedicated corpus show that summarization improves generative RE performance by reducing noise and guiding the model. However, BERT-based RE approaches still outperform generative models. This ongoing work demonstrates the potential of generative methods to support the study of specialized domains in low-resources setting.
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges
A proof-of-concept multi-agent GPT system for microbial protein literature extraction shows both fine-tuning and prompt engineering improve cosine-similarity scores, with fine-tuning slightly ahead but more variable.