arXiv subjects

Samuel Frontull

Publications and source records attributed to Samuel Frontull.

3 recordsLinked to original sources

arXiv2026

Inferring Empirical Sound Resource Bounds via Symbolic Execution and Linear Programming (Extended Version)

Existing approaches to resource analysis of programs can be classified into two main paradigms: static analysis and dynamic analysis methods. The former allow for formal guarantees but are inherently incomplete; the latter are widely applicable but may miss rare but characteristic (worst-case) scenarios and thus lack soundness. Hybrid approaches attempt to combine the strengths of both paradigms, thereby enabling the analysis of programs that are either too complex for purely static techniques or where dynamic approaches suffer from combinatorial explosion. In this paper, we present a novel hybrid approach that systematically derives upper bounds for the worst-case resource consumption of functional programs. Our method combines dynamic symbolic execution to exhaustively explore all possible computation paths within a constrained input space with mixed-integer linear programming to derive empirically sound upper bounds. We have implemented the methodology in a prototype tool, dubbed CompAS, which we made available on Zenodo.

cs.PL↗

arXiv2025

Compensating for Data with Reasoning: Low-Resource Machine Translation with LLMs

Large Language Models (LLMs) have demonstrated strong capabilities in multilingual machine translation, sometimes even outperforming traditional neural systems. However, previous research has highlighted the challenges of using LLMs, particularly with prompt engineering, for low-resource languages. In this work, we introduce Fragment-Shot Prompting, a novel in-context learning method that segments input and retrieves translation examples based on syntactic coverage, along with Pivoted Fragment-Shot, an extension that enables translation without direct parallel data. We evaluate these methods using GPT-3.5, GPT-4o, o1-mini, LLaMA-3.3, and DeepSeek-R1 for translation between Italian and two Ladin variants, revealing three key findings: (1) Fragment-Shot Prompting is effective for translating into and between the studied low-resource languages, with syntactic coverage positively correlating with translation quality; (2) Models with stronger reasoning abilities make more effective use of retrieved knowledge, generally produce better translations, and enable Pivoted Fragment-Shot to significantly improve translation quality between the Ladin variants; and (3) prompt engineering offers limited, if any, improvements when translating from a low-resource to a high-resource language, where zero-shot prompting already yields satisfactory results. We publicly release our code and the retrieval corpora.

cs.CL↗

arXiv2024

Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin

This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.

cs.CL↗