2026· International Conference on Language Resources and Evaluation· pp. 7178-7191· 0 citations· 47 references
Computer Science
TL;DR
Results from automatic and human evaluations show that fine-tuning on IMPaCTS improves performance both in terms of task completion and adherence to the targeted readability levels compared to few-shot baselines.
It is found that cue-based prompting can influence multilingual sentence-level Easy-to-Read simplification, but its benefits are modest, metric-dependent, and language-dependent.
Mehrzad Tareh, Horacio Saggion, Stefan Bott· 0 citations
CALAMITA is conceived as a rolling benchmark, enabling continuous integration of new tasks and models, and argues that this combination offers a blueprint for other languages and communities seeking inclusive and rigorous LLM evaluation practices.
Malvina Nissim, Danilo Croce, V. Patti et al.· Italian Journal of Computati...· 0 citations
TextQ-German is introduced, a novel dataset suite for human-centered evaluation of German NLG from a Quality of Experience (QoE) perspective, covering automatic text summarization and machine translation and develops automatic QoE prediction models, including transformer-based, linguistic feature-based, and hybrid appr...
Dinh Nam Pham, Shushen Manakhimova, Vivien Macketanz et al.· 0 citations
This work study large language model (LLM)-based simplification of scientific texts and presents a human-in-the-loop workflow that transforms expert summaries into more accessible versions for non-specialists.
A pipeline that uses large language models to extract grammatical rules, example sentences, and lexicons from grammar books and generate synthetic parallel corpora for fine-tuning-rather than feeding grammar content into prompts at inference time, as in prior work is introduced.
V. Ravikumar, Sina Ahmadi, L. Jäger et al.· arXiv.org· 0 citations
A prompt-based approach that closes the gap to fine-tuned models through three advances in GEC prompting methodology, and shows that batching multiple uncorrected sentences into a single input context acts as a targeted regularizer against overcorrection, systematically reducing the edit rate across diverse LLM familie...
Kateryna Karpo, Artem Chernodub· 0 citations
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