#digital-humanities News & Analysis

4 articles tagged with #digital-humanities. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · May 126/10

🧠

From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages

Researchers conducted a systematic evaluation of large language models for part-of-speech tagging in Medieval Romance languages, comparing them against traditional taggers. The study demonstrates that LLM-based approaches with fine-tuning and cross-lingual transfer learning significantly outperform conventional methods, offering practical applications for digital humanities research on historical texts.

AINeutralarXiv – CS AI · May 125/10

🧠

Matching Meaning at Scale: Evaluating Semantic Search for 18th-Century Intellectual History through the Case of Locke

Researchers evaluate semantic search as a tool for analyzing 18th-century intellectual history, specifically tracking how John Locke's ideas circulated through paraphrases and implicit references. While semantic search substantially outperforms traditional lexical methods at capturing meaning-level correspondences, linguistic analysis reveals that retrieval remains constrained by surface-level vocabulary overlap, suggesting both promise and limitations for historical corpus analysis.

AINeutralarXiv – CS AI · Apr 206/10

🧠

LLMbench: A Comparative Close Reading Workbench for Large Language Models

LLMbench is a new browser-based tool that enables detailed comparative analysis of large language model outputs through side-by-side visualization and token-level probability inspection. Unlike existing quantitative comparison tools, it applies digital humanities methodology to make the probabilistic structure of LLM-generated text legible through multiple analytical overlays and visualization modes.

AINeutralarXiv – CS AI · Apr 145/10

🧠

Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning

Researchers have developed GEVO, a glyph-driven fine-tuning framework for multimodal large language models designed to analyze the evolution of ancient Chinese characters. The study introduces a comprehensive benchmark with 11 tasks and over 130,000 instances, demonstrating that even smaller 2B-scale models can achieve significant performance improvements in understanding character evolution and historical text transformation.