A Dataset for Multidimensional Evaluation of French Synthetic Texts Generated by SLMs
Abstract
This dataset comprises synthetic news descriptions in French, generated from real newspaper headlines using a controlled Small Language Models (SLM) pipeline. The generation process follows three distinct configurations: Retrieval-Augmented Generation (RAG) and generation without external context (NO RAG) and QLoRA-based fine-tuning (FT). In the zero-shot configuration, the base model generates each description using only the corresponding headline and generation instruction. In the RAG configuration, the same base model additionally receives contextual information retrieved from a historical French news corpus. In the fine-tuning configuration, the models are adapted using headline-description pairs from the historical corpus through QLoRA and subsequently used to generate descriptions without external retrieval. For each configuration, three temperature settings (0.9, 0.6, and 0.3) were applied to control the variability and determinism of the generated outputs. The generation process relied on a corpus of real-world French news articles collected between February 16 and March 26, 2026. To prevent information leakage, the source data was chronologically strictly partitioned: Temporal Window Timeframe News Items Description Knowledge Window 16 Feb 2026 – 18 Mar 2026 7,767 Served as the RAG knowledge base and the QLORA fine-tuning dataset. Generation Window 18 Mar 2026 – 26 Mar 2026 2,071 Reserved exclusively for synthetic text generation and evaluation. The experimental…
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