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

2026-07-07 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

The integration of Large Language Models (LLMs) into Social Sciences and Humanities (SSH) research workflows, particularly for bibliographic discovery and literature synthesis, presents significant methodological, epistemic, and regulatory challenges. An ongoing use case, developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aims to adapt foundation models for SSH research practices. This initiative supports tasks such as question answering, comparative document analysis, and literature review. The evaluation framework adheres to the LLMs4EU protocol, incorporating both independent quantitative benchmarking for retrieval, summarization, traceability, and hallucination detection, alongside a qualitative assessment involving Digital Humanities experts. The project explores how domain-sensitive and regulation-aware generative AI can support SSH scholarship while preserving reliability and epistemic responsibility, by embedding model adaptation within structured legal and ethical compliance frameworks.

Key takeaway

For AI Scientists developing LLMs for specialized domains like SSH, you should prioritize domain-sensitive adaptation and integrate robust legal and ethical compliance frameworks from the outset. Your evaluation protocols must extend beyond standard benchmarks to include qualitative assessments by subject matter experts, ensuring reliability and epistemic responsibility. Consider multilingual access and disciplinary diversity as core design requirements to effectively support complex research tasks such as comparative document analysis and literature synthesis.

Key insights

Adapting LLMs for SSH research demands domain-sensitive, regulation-aware integration to address disciplinary diversity, multilingual access, and ethical challenges.

Principles

• Adapt LLMs for disciplinary diversity.
• Ensure multilingual source access.
• Combine quantitative and qualitative evaluation.

Method

Adapt foundation models for SSH tasks like Q&A and literature review. Evaluate using the LLMs4EU protocol, combining quantitative benchmarking (retrieval, summarization, traceability, hallucination) with qualitative assessment by Digital Humanities experts.

In practice

• Support SSH question answering.
• Facilitate comparative document analysis.
• Aid in literature review tasks.

Topics

• Large Language Models
• Domain Adaptation
• Social Sciences & Humanities
• Ethical AI
• Multilingual Processing
• Research Infrastructures

Best for: AI Scientist, Research Scientist, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.