Fine-Tuned Language Models Show Superior Structural Simplification Over Prompt Engineering, Study Finds

Global: Fine-Tuned Language Models Show Superior Structural Simplification Over Prompt Engineering, Study Finds

A recent preprint posted on arXiv on January 9, 2026, presents a comparative analysis of prompt-based versus fine‑tuned large language models (LLMs) applied to text simplification. The research, authored by Eilam Cohen, Itamar Bul, Danielle Inbar, and Omri Loewenbach, evaluates encoder‑decoder LLMs across several benchmark datasets to assess trade‑offs between the two paradigms.

Methodology Overview

The authors implemented both prompt engineering and full fine‑tuning on identical model architectures, then ran each variant through multiple standard simplification benchmarks. Evaluation employed a suite of automatic metrics covering structural changes, semantic similarity, and lexical simplicity.

Evaluation Metrics

Structural simplification was quantified using metrics such as SARI, while semantic fidelity was measured with BLEU and BERTScore. The study also tracked the degree of input copying, a known issue with prompt‑based approaches.

Key Findings

Results indicate that fine‑tuned models consistently achieve higher scores on structural simplification metrics, suggesting more effective content reduction and rephrasing. In contrast, prompt‑engineered models often obtain marginally higher semantic similarity scores but display a tendency to reproduce large portions of the original text.

Human Assessment

A parallel human evaluation, conducted on a subset of outputs, showed a clear preference for the fine‑tuned model’s simplifications, citing better readability and coherence despite occasional minor semantic deviations.

Resources Released

To support reproducibility, the authors have made publicly available the cleaned derivative dataset used in the experiments, checkpoint files for the fine‑tuned models, and the prompt templates employed for the baseline runs.

Implications for Future Research

The findings suggest that while prompt engineering remains a low‑cost entry point, fine‑tuning may be necessary for applications demanding substantial structural transformation. The released assets aim to facilitate further exploration of hybrid strategies that balance cost and performance.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.