Low-resource languages pose persistent challenges for Natural Language Processing tasks such as lemmatization and part-of-speech (POS) tagging. This paper investigates the capacity of recent large language models (LLMs), including GPT-4 variants and open-weight Mistral models, to address these tasks in few-shot and zero-shot settings for four historically and linguistically diverse under-resourced languages: Ancient Greek, Classical Armenian, Old Georgian, and Syriac. Using a novel benchmark comprising aligned training and out-of-domain test corpora, we evaluate the performance of foundation models across lemmatization and POS-tagging, and compare them with PIE, a task-specific RNN baseline. Our results demonstrate that LLMs, even without fine-tuning, achieve competitive or superior performance in POS-tagging and lemmatization across most languages in few-shot settings. Significant challenges persist for languages characterized by complex morphology and non-Latin scripts, but we demonstrate that LLMs are a credible and relevant option for initiating linguistic annotation tasks in the absence of data, serving as an effective aid for annotation.

Graph-based Retrieval-Augmented Generation (RAG) is increasingly used to explore long, heterogeneous, and weakly structured corpora, including historical archives. However, in such settings, naive full-corpus indexing is often computationally costly and sensitive to OCR noise, document redundancy, and topical dispersion. In this paper, we investigate corpus pre-targeting strategies as an intermediate layer to improve the efficiency and effectiveness of graph-based RAG for historical research.We evaluate a set of pre-targeting heuristics tailored to single-hop and multi-hop of historical questions on HistoriQA-ThirdRepublic, a French question-answering dataset derived from parliamentary debates and contemporary newspapers. Our results show that appropriate pre-targeting strategies can improve retrieval recall by 3–5% while reducing token consumption by 32–37% compared to full-corpus indexing, without degrading coverage of relevant documents.Beyond performance gains, this work highlights the importance of corpus-level optimization for applying RAG to large-scale historical collections, and provides practical insights for adapting graph-based RAG pipelines to the specific constraints of digitized archives.

This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.