arXiv subjects

Mihai Nadas

Publications and source records attributed to Mihai Nadas.

4 recordsLinked to original sources

arXiv2025

Evaluating Large Language Models for Diacritic Restoration in Romanian Texts: A Comparative Study

Automatic diacritic restoration is crucial for text processing in languages with rich diacritical marks, such as Romanian. This study evaluates the performance of several large language models (LLMs) in restoring diacritics in Romanian texts. Using a comprehensive corpus, we tested models including OpenAI's GPT-3.5, GPT-4, GPT-4o, Google's Gemini 1.0 Pro, Meta's Llama 2 and Llama 3, MistralAI's Mixtral 8x7B Instruct, airoboros 70B, and OpenLLM-Ro's RoLlama 2 7B, under multiple prompt templates ranging from zero-shot to complex multi-shot instructions. Results show that models such as GPT-4o achieve high diacritic restoration accuracy, consistently surpassing a neutral echo baseline, while others, including Meta's Llama family, exhibit wider variability. These findings highlight the impact of model architecture, training data, and prompt design on diacritic restoration performance and outline promising directions for improving NLP tools for diacritic-rich languages.

cs.CL↗

arXiv2025

Building Large-Scale English-Romanian Literary Translation Resources with Open Models

Literary translation has recently gained attention as a distinct and complex task in machine translation research, yet translation by small open models remains an open problem, particularly for low-resource languages such as Romanian. We introduce the TinyFabulist Translation Framework (TF2), a unified framework for dataset creation, fine-tuning, and evaluation in English $\to$ Romanian literary translation. Building on DS-TF1-EN-3M, the largest collection of synthetic English fables to date, our pipeline first generates 15k high-quality Romanian references from the TF1 pool using a high-performing large language model (LLM). We then apply a two-stage fine-tuning process to a 12B-parameter open-weight model: (i) instruction tuning to capture genre-specific narrative style, and (ii) adapter compression for efficient deployment. Evaluation combines a five-dimension LLM-based rubric (accuracy, fluency, coherence, style, cultural adaptation) as the primary comparative framework, alongside corpus-level Bilingual Evaluation Understudy (BLEU) reported as a secondary reference-based consistency metric. Our fine-tuned model (TF2-12B) achieves strong fluency and adequacy, narrowing the gap to top-performing proprietary models under automated and human-anchored evaluation, while being open, accessible, and significantly more cost-effective. We publicly release the fine-tuned model and two large-scale synthetic parallel datasets (DS-TF2-EN-RO-3M and DS-TF2-EN-RO-15K), along with all scripts and evaluation prompts. TF2 provides an end-to-end, reproducible pipeline for research on cost-efficient translation, cross-lingual narrative generation, and the broad adoption of open models for culturally significant literary content in low-resource settings.

cs.CL↗

arXiv2025

TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models

Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives with explicit ethical lessons. We present TF1-EN-3M, to our knowledge the first open dataset of three million English-language fables generated exclusively by instruction-tuned models no larger than 8B parameters. Each story follows a six-slot scaffold (character -> trait -> setting -> conflict -> resolution -> moral), produced through a combinatorial prompt engine that guarantees genre fidelity while covering a broad thematic space. A fully reproducible evaluation pipeline employs a panel of open-weight LLM judges from distinct model families, scoring grammar, creativity, moral clarity, and template adherence, complemented by reference-free diversity and readability metrics. Among ten open-weight generator candidates, an 8B-parameter Llama-3 variant delivers the best quality-cost trade-off, producing high-scoring fables on consumer hardware at approximately $0.135 per 1,000 fables. We release the dataset, generation code, evaluation scripts, and full metadata under a permissive license, enabling exact reproducibility and cost benchmarking. TF1-EN-3M opens avenues for research in instruction following, narrative intelligence, value alignment, and child-friendly educational AI -- demonstrating that large-scale moral storytelling requires neither proprietary giant models nor proprietary evaluation infrastructure.

cs.CL↗

arXiv2025

Synthetic Data Generation Using Large Language Models: Advances in Text and Code

This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant examples, these models can significantly augment or even substitute for real-world datasets, particularly in scenarios where labeled data is scarce, expensive, or sensitive. This paper surveys recent advances in leveraging LLMs to create synthetic text and code, highlighting key techniques such as prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. We examine how these methods can enrich low-resource tasks (e.g., classification, question answering) and facilitate code-centric applications (e.g., instruction tuning, code translation, bug repair) through automated verification of functional correctness. Alongside potential benefits - cost-effectiveness, broad coverage, and controllable diversity - we discuss the accompanying challenges, including factual inaccuracies in generated text, insufficient stylistic or distributional realism, and risks of bias amplification. Proposed mitigation strategies range from filtering and weighting synthetic outputs to reinforcement learning with execution feedback in code domains. We conclude by outlining open research directions, such as automated prompt engineering, cross-modal data synthesis, and robust evaluation frameworks, underscoring the growing importance of LLM-generated synthetic data in accelerating AI development while emphasizing ethical and quality safeguards.

cs.CL↗