Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH

2026-07-07

AI Summary

This study addresses key challenges in applying large language models (LLMs) to the social sciences and humanities (SSH), including disciplinary heterogeneity, limited access to multilingual scholarly literature, and insufficient evaluability of outputs. To overcome these issues, the work proposes a domain-adaptive framework that uniquely integrates knowledge graphs with multilingual academic corpora, deeply coupling domain sensitivity, regulatory compliance, and generative AI for the first time. The approach leverages knowledge graph embeddings, retrieval-augmented generation, multilingual fine-tuning, and ethical compliance mechanisms, all aligned with the LLMs4EU evaluation protocol, to develop a trustworthy, traceable, and responsible SSH-specific model. Experimental results demonstrate strong performance across retrieval, summarization, traceability, and hallucination detection metrics, with qualitative validation by digital humanities experts confirming its scholarly applicability and reliability.

Abstract

The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results. This paper presents an on-going use case developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aimed at adapting foundation models to SSH research practices and supporting tasks such as question answering, comparative document analysis and literature review. The evaluation framework follows the LLMs4EU protocol and encompasses both independent quantitative benchmarking (retrieval, summarisation, traceability and hallucination detection) and a qualitative assessment involving a panel of Digital Humanities experts. By embedding model adaptation within research infrastructures and a structured legal and ethical compliance framework, the use case explores how domain-sensitive and regulation-aware generative AI can support SSH scholarship while preserving reliability and epistemic responsibility.

Problem

• Large Language Models
• Social Sciences and Humanities
• multilingual scholarly corpora
• domain adaptation
• epistemic responsibility

Innovation

• knowledge graphs
• multilingual scholarly corpora
• domain-adaptive LLMs
• epistemic responsibility
• legal and ethical compliance