Recent language models (LMs) that follow instructions have showcased remarkable abilities to tackle diverse natural language processing (NLP) tasks, given appropriate prompts. However, the potential of these models for Automatic Text Simplification (ATS) in Italian remains largely unexplored. In this paper, we pioneer the first in-depth investigation into the capabilities of LMs for performing ATS in Italian. We evaluate six state-of-the-art models on a benchmark Italian ATS dataset of administrative texts, reporting six readability metrics on the generated text. Our findings demonstrate a large variability across models, scales, and prompts. Among the tested models, GPT-3.5 editing capabilities are the most suitable, outperforming, surprisingly, human-written simplification. Furthermore, we shed light on the enigmatic multilingual capabilities of instruction following models, opening up new avenues for research in this domain.
els, e.g., T5 [
Using recent large-scale language models (LMs) is a Contribution We propose the first in-depth study on promising direction in this context. In particular, recent whether current IFT models can simplify written pasevidence has shown that high-capacity pretrained mod- sages in Italian. We report a large variability across models, with proprietary GPT-3.5 being the most suitable solution. In addition, we introduce a novel metric to better account for accurate and simple generations. We
[12]. Sentences were manually simplified both at lexical and syntactic levels. • Rewritten Docs (Admin-It): 48 pairs of sentences (∼ 7% of the total dataset) from administrative documents collected and simplified by Cortelazzo [13]. Sentences were rewritten according to linguistic simplification and communicative efectiveness criteria.
release code and data to facilitate future research.1
putational linguistics that studies methods and techniques to simplify textual content [10]. This task involves transforming complex or dificult-to-understand text into more straightforward and accessible language. Automatic text simplification has been viewed as a critical technique for increasing the inclusion of people with special needs and boosting social inclusion [10].
To simplify a text, strategies might involve sentenceor word-level interventions (e.g., breaking down longer passages into multiple sentences or changing less common words with easier equivalents). Most importantly, such edits can be learned, and NLP models can be applied to automate and generalize.
Automatic text simplification has typically focused on two distinct tasks: lexical simplification and syntactic simplification, each of which addresses a diferent subproblem in the larger task of making texts easier to read and understand [10]. The goal of lexical simplification is to make a document easier to understand by either changing the vocabulary to use terms that are more likely to be familiar to the reader or by providing clearer definitions of unfamiliar words. Whereas the purpose of syntactic simplification is to detect syntactic phenomena in phrases that may obstruct readability and understanding, with the hope of rewriting the sentence in a way that makes it easier to read and comprehend (by, for example, changing it from the passive to the active voice).
NLP tasks verbalized to natural language [14]. The verbalization follows a task-dependent template–e.g., “Translate the following sentence from
Flan-T5-XXL Vicuna v1.3 Camoscio Guanaco Llama 2 Chat* GPT-3.5*
prompting-italian-text-simplification
RLHF data, IFT models have shown multilingual abilities, such as solving cross-lingual tasks (e.g., machine translation), or understanding and providing coherent responses to non-English input queries [20].
We leverage this finding and prompt models to run text simplification in Italian in a zero-shot setup. Specifically, we compile a request for simplification using a given prompt template, feed it to the model, and take the model response unmodified. For Vicuna, Guanaco, Llama 2 Chat, and GPT-3.5 we use model-specific system message templates (see Appendix A). We specify no system message or use any prompt template for Flan-T5 and Camoscio.
Figure 1 displays a system overview.
Temperature Top P Top K Repetition Penalty Penalty Alpha Length Penalty Max new tokens {src_lang} to {tgt_lang}: {src_text}” is one of the template used for machine translation.
Although FLAN does not include specifically tasks related to language simplification, we hypothesize that 1) pretraining data, 2) the presence of tasks that share some of the traits (e.g., summarization), and 3) scale enable models to simplify language. We experiment wtih FlanT5-XXL (11B), the largest T5-based FLAN model.
diferent prompts elicit multilingual capabilities diferently [21]. Therefore, we experiment with two templates, both starting with a prefix stating the task followed by 3.2. LLaMA IFT Models the passage to simplify.
In our explicit template (Template-EN), we state overtly
Since LLaMA [
We test Vicuna v1.3 7B, 13B, and 33B [15]. These mod- is the passage to simplify. We also experiment with els have been trained with IFT on a corpus of around 70K an implicit template (Template-IT), where the entire conversations from the ShareGPT website.2 We test also prompt is written in Italian to hint models to reply Guanaco (65B) [16], an IFT model fine-tuned on around in the same language: “Semplifica il testo 10K conversations from the Open Assistant project.3 seguente.\n{src_text}” (eng: “Simplify the follow
As an additional baseline for the Italian, we include ing text.”).
Camoscio [17], a LLaMA model instruction fine-tuned on samples exclusively in Italian. The fine-tuning corpus includes around 52K instructions from the Alpaca dataset [18] machine-translated with GPT-3.5.
introduces an additional step to the standard IFT pipeline. After the supervised fine-tuning stage, a policy learning step maximizes the alignment with human preferences by teaching the model to produce responses that are more likely to be preferred by human users [19].
We experiment with GPT-3.5 [7, gpt-3.5-turbo, last accessed June 15, 2023] and Llama 2 Chat (70B) [8].
ration, loosely inspired by Vicuna’s Chat Arena4 for all the models. Table 2 reports the generation configuration used. We use models and code as released in HuggingFace transformers [22] and simple-generation [23] to run inference.
cations using benchmark readability metrics, which we categorize into traditional, namely Flesh-Kincaid test and GulpEase index, and READ-IT-based metrics.
The Flesh-Kincaid test (↑) [24] is a widely utilized measure in education for assessing the readability level of books. In this context, we refer to the Flesch-Vacca formula, designed specifically for Italian text.
Admin-It Eval Metric READ-IT
Gulpease Flesch–Kincaid
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Simplify the following text. Write only the response, and in Italian.
Il richiedente, che al momento della presentazione della richiesta ha optato per il bonifico su conto corrente bancario o postale o per l’accredito su carta prepagata, riceverà l’importo totale spettante direttamente sull’iban indicato nella domanda. ### Assistant: Il richiedente riceverà l'importo totale direttamente sull'IBAN indicato nella domanda se ha scelto il bonifico su conto bancario o postale o l'accredito su carta prepagata al momento della presentazione della richiesta.
Model Flan-T5-XXL
Vicuna
Guanaco Llama 2 Chat
Camoscio GPT-3.5
The GulpEase index (↑) [25] calculates text readabil- aforementioned variations. ity based on factors such as word length (measured in letters), number of words, and sentence length. It does not have a direct association with any particular language. 6. Results
READ-IT (↓) [26] is a machine learning-based readability metric. The model has been trained to evaluate This section illustrates the results of prompting instructhe readability of a text using various features. Diferent tion following models for generating simplified versions variations of the READ-IT metric exist: base employs of an input text. We first perform a preliminary investibasic features like sentence and word length; lexical fo- gation on the generated outputs. Based on this analysis, cuses on lexical features, such as vocabulary complexity; we discover that the benchmark readability metrics are syntax considers grammatical features like syntactic tree not ideal in our setup, as models produce non-relevant depth and part-of-speech categories; all combines all the responses. Therefore, we propose a novel adjusted score Gulpease to better measure improved readability and adherence to Table 3 presents the scores for each model, including the original text. variations in Italian and English prompt templates where applicable (see Section 4). As observed in the two exam6.1. Inspecting Generated Responses ples, only two models, Flan-T5-XXL and GPT-3.5, demonstrate reasonable BERTScore and % Detected Italian Tables 5 and 6 (Appendix B) illustrate two examples ex- metrics. It is crucial to emphasize the discouragingly low tracted from the Admin-It dataset. In the first instance, % of Italian generations by Llama 2 Chat and the unsatthe complex sentence uses administrative jargon related isfactory BERTScore of Camoscio. Additionally, when to numbers and dates, while the manually-simplified text testing the models with both template configurations, conveys the same concept using more straightforward the Italian template tends to yield slightly better results. verbs, e.g., “assumere l’uficio di” ( eng: to get the role of) As a consequence, moving forward, we will consider the is replaced with “essere” (eng: to be). However, the model- Template-IT model version whenever available. generated texts exhibit undesired behaviors: the automatic simplifications are not consistently simpler, some 6.2. Automatic Text Simplification Results are not written in Italian, some result in drastically longer passages, incorporate prompt-related content, or occa- Table 4 presents the text readability metrics (see Section sionally add irrelevant information. The second example 5) for the original (complex) text, its reference humanpresents a similar case, wherein the model-generated written simplification, and the model generations. Out of simplification includes code, questions, and apparent er- all models, only three — Camoscio, Guanaco, and GPTrors likely produced by incorrect translations, e.g., “il 3.5 — consistently exhibit readability metrics better than bambino deve essere vivo” (eng: the child must be alive). human simplification.
To investigate the issues raised in our initial qualita- Interestingly, Guanaco yielding the best results in each tive analysis, we conducted two investigations. First, we individual metric is contradicting our findings from the calculated the adherence of the model-generated sim- previous section. The issue lies in the fact that the plifications to the human-written reference simplifica- readability metrics alone do not account for cases tion provided in Admin-It. This metric helps us identify when models produce unrelated or inaccurate gencases where the produced simplifications diverge from erations. For instance, the Guanaco generation shown the source text, potentially containing code or unrelated in Table 6 may be a highly readable sentence (READquestions. For this evaluation, we used BERTScore, a lan- IT = 96) but has very low adherence to the original guage generation evaluation metric based on pretrained text (BERTScore = 0.63).
BERT contextual embeddings [27]. Second, we measured To address this issue, we introduced a novel READthe percentage of times the model-generated simplifica- IT metric which also takes into account the original tions are in Italian (% Detected Italian). To accomplish text similarity, named READ-IT_. The metthis, we used the Python langdetect5 library. We clas- ric is computed as the product among READ-IT and sified a text as Italian if the library detected the Italian BERTScore. By using READ-IT_, we identify language with a confidence level higher than 0.99. GPT-3.5 as the best model across the board. This finding aligns with our qualitative investigation. Moreover, it suggests that open LLaMA- and FLAN-based instruction following models lag far behind proprietary GPT alternatives, and we do not encourage their use for zero-shot ATS in Italian.
tion have been long studied for English, with works spanning from statistical machine translation-based systems [28] to supervised recurrent neural networks [29, 30], graph convolutional neural networks [31], and Transformer encoders [32]. Acknowledgments
Similar eforts for the Italian language have seen a joint development of corpora, ATS models, and evalua- This project has in part received funding from Fondazione tion metrics. Brunato et al. [33] designed the first parallel Cariplo (grant No. 2020-4288, MONICA) and the Euresource, collecting two sets of pairs where several sen- ropean Research Council (ERC) under the European tences are simplified following diferent guidelines and Union’s Horizon 2020 research and innovation program for diferent target audiences. Other examples are the (No. 949944, INTEGRATOR). The authors are members PaCCSS-IT [34], SIMPITIKI [11], and Admin-It [9] cor- of the MilaNLP group and the Data and Marketing Inpora, among others. We focus on the Admin-It corpus, sights Unit of the Bocconi Institute for Data Science and which covers the particularly verbose and complex ad- Analysis. ministrative language across diferent types of simplification edits. ERNESTA [35] is the first documented solution for Italian ATS, specifically addressing simplification for References children with low reading skills. The system simplifies by making anaphoras explicit and performing sentencelevel edits, such as splitting into simpler units, deleting redundant information, and more. Subsequent approaches adapt rule-based systems to Italian [36] or fine-tune a small transformer encoder on a machine-translated parallel corpus [37]. Surprisingly, no transformer-based endto-end approaches have been proposed recently for ATS on original Italian corpora. This paper presents the first attempt at using large-scale language models. for instance, non-factual and non-truthful generations when prompting language models about world knowledge [38, 39, 40, inter alia]. We do not control for factuality and relevance in the generated simplification and we cannot exclude that some might alter content and meaning. As we discussed in Section 6.2, we advocate for new comprehensive evaluation procedures that account for artifacts that stochastic language model can introduce.
Moreover, instruction fine-tuned language models are known to encode social biases and generations might reflect them [41, 42, 43, inter alia].
ity of large-scale instruction following models to simplify Italian administrative sentences. The outcomes demonstrate that, when it comes to Italian ATS, open-source models are significantly behind proprietary GPT alternatives.
limitations comes with limitations and risks. On the one hand, generations are the result of a stochastic decoding process and coherence, relatedness, and factuality cannot be directly controlled. Multiple evidence reported,
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31–41. [44] A. Lacoste, A. Luccioni, V. Schmidt, T. Dandres, [34] D. Brunato, A. Cimino, F. Dell’Orletta, G. Venturi, Quantifying the carbon emissions of machine learnPaccss-it: A parallel corpus of complex-simple sen- ing, arXiv preprint arXiv:1910.09700 (2019). tences for automatic text simplification, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016, pp. 351–361. A. System Messages [35] G. Barlacchi, S. Tonelli, Ernesta: A sentence simplification tool for children’s stories in italian, in: We used standard system messages and prompt templates Computational Linguistics and Intelligent Text Pro- for each tested models. We retrieved all templates from cessing: 14th International Conference, CICLing https://github.com/lm-sys/FastChat. 2013, Samos, Greece, March 24-30, 2013, Proceed- The following is the list of system messages used in ings, Part II 14, Springer, 2013, pp. 476–487. our experiments (see Figure 1): [36] C. Scarton, A. Palmero Aprosio, S. Tonelli,
T. Martín Wanton, L. Specia, MUSST: A multilingual • Vicuna: “A chat between a curious human and syntactic simplification tool, in: Proceedings of the an artificial intelligence assistant. The assistant IJCNLP 2017, System Demonstrations, Association gives helpful, detailed, and polite answers to the for Computational Linguistics, Tapei, Taiwan, 2017, human’s questions.” pp. 25–28. URL: https://aclanthology.org/I17-3007. • Guanaco: “A chat between a curious human and [37] A. L. Megna, D. Schicchi, G. L. Bosco, G. Pilato, an artificial intelligence assistant. The assistant A controllable text simplification system for the gives helpful, detailed, and polite answers to the italian language, in: 2021 IEEE 15th International user’s questions.” Conference on Semantic Computing (ICSC), IEEE, • Llama 2 Chat: “You are a helpful, respectful and 2021, pp. 191–194. honest assistant. Always answer as helpfully as [38] M. Zhang, O. Press, W. Merrill, A. Liu, N. A. Smith, possible, while being safe. Your answers should How language model hallucinations can snowball, not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don’t know the answer to a question, please don’t share false information.” • GPT-3.5: You are a helpful assistant.
• Flan-T5, Camoscio: None
ture, which has a carbon eficiency of 0.29 kgCO 2eq/kWh. A cumulative of 1 hour of computation was performed on hardware of type A100 PCIe 80GB (TDP of 250W).
Total emissions are estimated to be 0.07 kgCO2eq of which 0 percents were directly ofset.
Estimations were conducted using the MachineLearning Impact calculator presented in [44].
Simplified Text Camoscio Flan-T5-XXL
Non possono assumere l’uficio di giudice popolare (art. 12 legge 10-4-1951, n. 287); Non possono essere giudici popolari: tuttavia, sono autorizzati a dare la loro opinione sulla questione in corso.Translation:Semplifica il testo seguente.I magistrati popolari non potranno assumersi (art. 12 legge 10-4-1951, n. 287); tuttavia, possono dare la loro opinione su un caso aperto.
Non possono assunire l’uficio di giudice popolare (art. 12 legge 10-4-1951, n. 287); I cittadini non possono diventare giudici popolari.
In Italia, l’articolo 12 della legge 287/1951 prevede che i magistrati non possano assumere l’uficio di giudice popolare. Questa norma è stata introdotta per garantire l’imparzialità e l’indipendenza del processo giudiziario e per evitare che i magistrati siano influenzati dai sentimenti popolari o dalle pressioni esterne. Tuttavia, in alcuni casi specifici, come ad esempio in caso di necessità urgente o in assenza dei giudici popolari previsti dalla legge, i magistrati possono essere chiamati a far parte di un tribunale popolare.### Human: Cosa significa "necessità urgente"?### Assistant: La locuzione "necessità urgente" nel contesto della legislazione italiana può avere diverse connotazioni a seconda del contesto in cui viene utilizzata. In generale, si riferisce a una situazione in cui è necessario agire rapidamente e con urgenza per evitare conseguenze gravi o impreviste.In particolare, nell’ambito della giustizia, la necessità urgente può riferirsi a situazioni in cui è necessario afrettare il processo o prendere provvedimenti immediati per salvaguardare l’ordine pubblico o la sicurezza delle persone. Ad esempio, in caso di emergenze sanitarie o di disastri naturali, i magistrati possono essere chiamati a interrompere le attività ordinarie per afrontare le conseguenze immediate della situazione.
Thank you for your question. However, I must point out that the question contains harmful and discriminatory language. The term "giudice popolare" is not appropriate and could be ofensive to certain individuals. Additionally, the question implies that a person cannot hold a public ofice based on their identity, which goes against the principles of equality and non-discrimination.Instead, I suggest rephrasing the question in a respectful and inclusive manner. For example, "What are the requirements to hold a public ofice, and how can one ensure they meet them?" This way, we can focus on providing helpful and informative responses without perpetuating harmful stereotypes or biases. 这句话是抒发性语言，意思是在《法律10月4日1951年287号》中规定，普通公民不能被任用为法巫 高手或法官（判官）。 1. Non possono assumere l’uficio di giudice popolare (art. 12 legge 10-4-1951, n. 287). 1. Non possono assumere l’uficio di giudice popolare (art. 12 legge 10-4-1951, n. 287)Suggerimenti:Non sono idonei ad essere eletti come giudici popolari coloro che risultano insussidiati o destinatari di misure di prevenzione restrittiva della libertà personale, nonché i soggetti con finalità di lucro e le persone giuridiche ad essi correlate ai sensi dell’articolo 3, comma 1-bis del decreto legislativo 3 aprile 2006, n. 158. Inoltre, non possono assumere tale uficio i magistrati in servizio attivo, gli avvocati, i procuratori, i notai e i commercialisti.
Se non è possibile, indica "non può essere determinato".<p>Il bambino <strong>residente</strong>con almeno uno dei suoi genitori deve dimostrare la sua residenza in modo permanente nel paese.</p> Il bambino deve essere vivo con almeno uno di i bambini.
Il bambino deve vivere con almeno un genitore.
Scusi, ma non ho capito la relazione tra la prima frase e la seconda. Cosa vuole sapere esattamente? Mi dispiace, pero la tua richiesta non è chiara e coherente. "Il bambino deve risultare residente con almeno un genitore" non è una frase completezza e non è chiaro a cosa si riferisca. Per favore, fornisci ulteriori informazioni o contesti per consentirmi di comprendere meglio la tua domanda e fornirti risposta adeguata. 1. Il padre, che lavora come medico in una clinica di campagna, e la madre, che gestisce un negozio di artigianato in città.2. Il padre, che lavora per l’Unione Europea come funzionario nella sede di Bruxelles, e la madre, che è professore universitario di filosofia presso l’Università del Nord.3. Il padre, che lavora come pilota di elicotteri per la Croce Rossa, e la madre, che è infermiera in un ospedale di emergenza. Ecco l’algoritmo corretto considerando che il bambino deve risultare residente con almeno un genitore:“‘csharp# Impostazioni del database e dei costruttiimplicit noneinteger, parameter :: n = 10000integer :: iwork(n)integer :: parent(2), child(2), sibling(2)integer, dimension(n) :: populationinteger :: parents(n), children(n), rank! Definizione della funzione per costruire la matrice di riproduzionefunction intra_familia(parents) integer, intent(in) :: parents(n) integer :: sum, count sum = 0 count = 0 do while (count <size(parents, 1)) if (parents(count + 1) == parents(count)) then sum = sum + parents(count) count = count + 1 else count = count + 1 end if end do intra_familia = sumend function! Creazione degli array globalparents = zeros(n)children = zeros(n)ranks = zeros(n)! Selezioniamo casualmente i primi N individuiiwork = sort(1:n)do i = 1, n if (mod(iwork(i), 10) == 0) then parents(i) = iwork(i) end ifend do ! Calcoliamo le parentiadi tra gli individuifor i = 1:n { for j = i+1:n { if (parents(i) == parents(j)) then continue end if child(j) = i }}! Inizializziamo la matrice delle frequenzefor i = 1:n { population(i) = 1}! Applichiamo la selezione incrociata su una generazionen_eliminati = 0do i = 1:n { non_selected(i) = 0 1. Oggetto: Modifica del comma 2 dell’articolo 4 della Legge 20 maggio 1975 n. 151 per la determinazione della residenza del minore in modo da richiedere l’iscrizione del bambino alle liste elettorali nei comuni ove risiedono almeno uno dei genitori.Nota: Questo oggetto si riferisce alla modifica della normativa vigente in cui il minore deve essere iscritto come residente solo se è riconosciuto come figlio legittimo e se il padre o la madre sono residenti nel comune e apporta le modifiche necessarie per includere anche i casi in cui il minore vive con uno o entrambi i genitori non sposati o il padre o la madre sono residenti in altri luoghi.
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