Wikipédia a des lacunes systématiques dans sa couverture des langues peu dotées ainsi que des groupes sous-représentés (par exemple, les femmes). Cet article présente un nouvel outil pour soutenir les efforts visant à combler ces lacunes en générant automatiquement des débuts d’articles en anglais, français et irlandais, et en facilitant la post-édition et la mise en ligne sur Wikipédia. Un générateur basé sur des règles et un LLM sont utilisés pour générer deux articles alternatifs à partir de graphes de connaissances DBpedia ou Wikidata sélectionnés par l’utilisateur, permettant à l’article généré via LLM, souvent plus fluide mais plus sujet aux erreurs, d’être vérifié en termes de contenu par rapport à l’article généré par des règles, plus fiable, mais moins fluide. Le code de l’outil est disponible sur https://github.com/dcu-nlg/wiki-gen-demo et il est actuellement déployé sur http://ec2-18-224-151-90.us-east-2.compute.amazonaws.com:3000/.

As the cost of training ever larger language models has grown, so has the interest in reusing previously learnt knowledge. Transfer learning methods have shown how reusing non-task-specific knowledge can help in subsequent task-specific learning.In this paper, we investigate the inverse: porting whole functional modules that encode task-specific knowledge from one model to another. We designed a study comprising 1,440 training/testing runs to test the portability of modules trained by parameter-efficient finetuning (PEFT) techniques, using sentiment analysis as an example task. We test portability in a wide range of scenarios, involving different PEFT techniques and different pretrained host models, among other dimensions. We compare the performance of ported modules with that of equivalent modules trained (i) from scratch, and (ii) from parameters sampled from the same distribution as the ported module.We find that the ported modules far outperform the two alternatives tested, but that there are interesting differences between the four PEFT techniques tested.We conclude that task-specific knowledge in the form of structurally modular sets of parameters as produced by PEFT techniques is highly portable, but that degree of success depends on type of PEFT and on differences between originating and receiving pretrained models.

Wikipedia is known to have systematic gaps in its coverage that correspond to under-resourced languages as well as underrepresented groups. This paper presents a new tool to support efforts to fill in these gaps by automatically generating draft articles and facilitating post-editing and uploading to Wikipedia. A rule-based generator and an input-constrained LLM are used to generate two alternative articles, enabling the often more fluent, but error-prone, LLM-generated article to be content-checked against the more reliable, but less fluent, rule-generated article.

Our submission to the GEM data-to-text shared task aims to assess the quality of texts produced by the combination of a rule-based system with a language model of reduced size, by first using a rule-based generator to convert input triples into semantically correct English text, and then a language model to paraphrase these texts to make them more fluent. The texts are translated to languages other than English with the NLLB machine translation system.