In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages
Authors: Asım Ersoy
Gerson Vizcarra
Tahsin Mayeesha
Benjamin Muller
Venue: EMNLP 2023 Findings (2023)
Keywords: Multilingual language models
language models
formality bias analysis
bias analysis in language models
TL;DR: This work analyzes the formality level of two multilingual language models: XGLM and BLOOM, across five languages, namely Arabic, Bengali, English, French, and Spanish.

Abstract: Multilingual generative language models (LMs) are increasingly fluent in a large variety of languages. Trained on the concatenation of corpora in multiple languages, they enable powerful transfer from high-resource languages to low-resource ones. However, it is still unknown what cultural biases are induced in the predictions of these models. In this work, we focus on one language property highly influenced by culture: formality. We analyze the formality distributions of XGLM and BLOOM's predictions, two popular generative multilingual language models, in 5 languages. We classify 1,200 generations per language as formal, informal, or incohesive and measure the impact of the prompt formality on the predictions. Overall, we observe a diversity of behaviors across the models and languages. For instance, XGLM generates informal text in Arabic and Bengali when conditioned with informal prompts, much more than BLOOM. In addition, even though both models are highly biased toward the formal style when prompted neutrally, we find that the models generate a significant amount of informal predictions even when prompted with formal text. We release with this work 6,000 annotated samples, paving the way for future work on the formality of generative multilingual LMs.

In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages
Asm Ersoyle 1 Gerson Vizcarra** Tasmiah Tahsin Mayeesha Benjamin Muller
Huawei Türkiye R&D Center BBacco de Crédito e Inversiones
North South University "Sorbonne Université @huawei. 1aa..eeessyy@huaei...... com gersonw. www.wvviaaaaaaaa.cccom tasmiah, tahsin@nor thsouth. edu
asim. ersoy
Abstract
level of formality can be useful for different NLP applications (Hovy and Yang, 2021). For example, controlling the tonc machine translation models (Sennrich et al., 2016; Niu et al., 2017; Feely et al., 2019), designing chatbots with formality aware- ness to respond to user-preferred conversational style (Cox and Ooi, 2022), or assisting users to change the formality level of their writings (Rao and Tetreault, 2018; Wang al., 2019, 2020).
Multilingual generative language models (LMs) are increasingly fluent in large variety of languages. Trained on the concatenation of corpors in multiple languages, they enable pow- erful transfer from high-resource languages to low-resouree oncs. However, it is still unknown what cultural biases are induced in the predictions of these models. In this work, we focus one language property highly influenced by culture: formality. We analyze the formality distributions pop-
of XGLM and BLOOM's predictions, ular generative multilingual language models, languages. We classify 1,200 gencrations per language formal, informal, incohesive and measure impact of the prompt formality on the predictions. Overall, we observe diversity of behaviors across models languages. For instance, XGLM generates informal text Ara- and Bengali when conditioned with informal prompts, much more than BLOOM. addition, even though both models are highly biased toward the formal style when prompted neutrally, we find that the models generate a significant amount of informal predictions even when prompted with formal text. We release with this work 6,000 an- notated samples, paving the way for future work on the formality of generative multilingual LMs.
Generative language models have demonstrated capabilities in producing cohesive texts and solving NLP tasks with zero/few-she learning (Radford et al., 2019; Brown al., 2020b; Chowdhery et al., 2022; Zhang et al., 2022), even in multilingual scenarios (Lin al., 2021b; Scao al., 2022; Bar- bieri et al., 2022: Jiang al., 2022; Anil et al., 2023). Multilingual language models are trained with large amounts of text from different sources. That training process could make the model bi- ased towards a certain level of formality because of the data of each language as well as cross-lingual transfer (Pires et al., 2019; Libovický et al., 2020; Muller al., 2021), limiting the capabilities of the model to adapt the different cultures of an NLP application.
1 Introduction
Natural Language Processing (NLP) systems are used worldwide across multiple cultures, audiences, contexts, communication goals, demographics, and languages. Thus, it is essential that these models be able to adapt the sociocultural context of its users. As described by Hershcovich et al. (2022), linguistic style is one of the major dimensions by which cultures vary in NLP technologies.
This work analyzes the formality level of two multilingual language models: XGLM (Lin et al., 2021b) and BLOOM (Scao et al., 2022), across five languages, namely Arabic, Bengali, English, French, and Spanish. To do so, native/proficient speaker of each language evaluates the generation outputs of each model into three categories: formal, informal, and incohesive. This evaluation allows us to analyze the generations across three different di- mensions: the cohesiveness of the generations, the formality bias given neutral prompts, and the for- mality preservation given formal/informal prompts. As example, show in Table I the predictions of BLOOM and XGLM conditioned on the same
In this work, we focus on formality. Formality is a stylistic property of language that can impact how we perceive text. It typically carries informa- tion about the culture of the speaker (or writer), is constrained by the context of the message, and can impact the communicative goal of a text (Heylighen and Dewaele, 1999). Generating text with a desired
"qqual contribution. This work was done as part of the Fatima Followship mentoring program.
In short, we define sequence as incohesive if it cannot he evaluated as formal/informal More details in Section 4.3

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Generation
Prompt
XGLM(7.5B)
BLOOM(7.I)
कोमलड सा भाताहा पाान काट.
कमा I क बा a शरमक भानमशा c @ पागाग बर Cocs कड आमानन सात शा पाान
काा पनंप w लIR भर[bल c काााड cote काकाкय रान शारनलामरर सा cueS शह करमन
राल 2 किies हिीर शहकनरर शागर भाब शात c अकयर
कपामर पाडनरल नाड पाक Gा GरI CRTCRR
Not being able to cry also very difficult
But people of our hopes and dreams change so much! There could not be greater exam- ple...
But Anju Ghosh was the one who broke down in tears, her daughter said. Anju Ghosh returned work after spending time with her daughter. Aftal Hossain, veteran actor and theater personality of the country, died at Tongi Government Hospital last Thursday around 9 pm.
Table I: XGLM (Lin et al., 202 and BLOOM (Scao al., 2022) generating predictions of different formality (informal in red and formal in green) based Bengali formal prompt sampled from the InFormal dataset (Krishna et al., 2022). As illustrated here, the formality of the predictions differs between XGLM and BLOOM.
prompt in Bengali but generating text of different formality level. Overall, our contributions the following:
neutral way. However, both models are very sensitive to the formality of the prompt and will generate informal text if conditioned with an informal prompt. This particularly strik- ing for Arabic: BLOOM generates dialectal Arabic (considered informal) when prompted with informal text while being extremely bi- ased toward Modern-Standas Arabic (consid- ered formal).
the best of our knowledge, this the first work to analyze the formality of generative multilingual language models across multi- ple languages. While we have focused on specific models and languages in this work, the procedures followed to define formality, prompt sourcing, language generation, and measurement of how formality is preserved from prompts are generalizable to any gener- ative system and language. We open-source 1,200 generations, 2 per language, manually annotated as formal, informal, or incohesive.
Formality Across Different Languages
We start by defining formality in the five languages of our study.
The Arabic language is spoken in many dialects (Watson, 2011). These dialects are vari- ants of classical or standard Arabic, which has a modernized version of it called Modern Standard Arabic (MSA), Badawi (1973), in his famous book "Mustawayat Al-arabiyya Al-muasira Fi Misr" The levels of contemporary Arabic Egypt, presents a theory on the relationship between standard Arabic (Fusha) and vernacular Arabic (Ammiya) Egypt. His theory describes the situation as a continuum with major divisions: illiterate colloquial Ara- bic, educated colloquial Arabic, elevated colloquial Arabic, modern standard Arabic, and classical Ara- bic. The first three divisions Ammiya, which is considered informal and not necessarily gram- matically correct. The last two divisions are Fusha, which is considered formal. However, the defini- tion of what is formal and what is informal could depend on the problem at hand; for example, in one
Arabic
We find that BLOOM generates about twice as long texts as XGLM. Besides, almost all the generated formal sentences are longer than the informal ones. Also, informal generations in English, French, and Spanish charac- terized by being more conversational, and in Bengali, by having more punctuation marks.
We find that BLOOM is significantly more cohesive than XGLM in English, French, and Spanish and performs similarly other lan- guages.
Both XGLM and BLOOM are generally bi- ased toward formal text when prompted in a
'https://github com/as com/asimokby/ bimallty-bbis--ai
biraality-bias--aalyiis analysis

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case, elevated colloquial Arabic could be consid- ered formal, while illiterate colloquial Arabic as in- formal. In our work, we define formality for Arabic as follows: a piece of text is formal if it contains no words coming from any Arabic dialect which is not considered as Fusha, following (Badawi, 1973)'s definition of Fusha. For example, the following sentence: مسسجدو 4 أقرب أبن (where is the closest mosque?) is formed of only Fasih, formal, words. Similarly, a piece of text is informal if it contains a word coming from any dialect and not Fusha. For example, Suquيa أقرب فبن - (where is the closest mosque?) is informal because of the word فين (where) which is Egyptian Arabic.
like Sylheti, Chakma, and Chittagonian are gener- ally considered informal while classical Bengali dialect (Sādhubhāsā) or standardized Bengali di- alect (Cholito vasha) is considered formal (Ray et al., 1966).
English Formality in English is commonly de- fined as the language style used a given situation. A formal speech, for instance, has a very careful selection of pronunciation, words, and structure (Richards and Schmidt, 2013). Heylighen and De- waele (1999) divide English formality into two di- mensions: deep formality, characterized by the understanding of the precise meaning, avoiding am- biguity; and surface formality which focuses on the rigorous selection of manners. Some recent works focus on the latter to evaluate formality us- ing the selection of words (Brooke et al., 2010) and discarding the topic (Pavlick and Tetreault, 2016). Following Liardét et al. (2019), we use the follow- ing rules below to evaluate cohesive English text as informal: (i) Presence of contractions, abbre- viations, and colloquial expressions; (ii) presence of grammar infelicities, that is, unsuitable expres- sions, inconsistencies in writing, and misspellings; (iii) high occurrence of delexical verbs and phrasal verbs; and (iv) higher involvement of human partic- ipants and subjective judgments, such as opinions.
Bengali Bengali has complex and elaborate sys- tem of using pronouns express the degrees of familiarity and formality between the participants in a conversation (Das, 1968; Uddin, 2019). T- V distinction (Brown et al., 1960) or the contex- tual usage of pronouns to convey varying levels of formality, familiarity, and politeness, which is found in many Romance languages (French, Ital- ian, Spanish, etc.), can also be seen in Bengali. Bengali follows tripartite form of second-person pronouns: /ारनर Apni (formal) for respected elders and strangers, शी Tumm (polite) for sib- lings/friends or familiar people and ğ Tui (in- formal) for those who are younger, children or very close friends. The third person he she can be trans- lated to शिन\ / Tini (formal) vs ( / Se (informal), which encodes two levels of formality honorific and non-honorifie. Bengali pronouns numbers such as singular/plural, but the notion of formality is not changed by gender or numerical properties (David, 2015).
French Formality is typically classified in French into three classes: soutenu, courant and familier (Gadet, 2005; Beeching et al., 2009). The register soutenu is reserved for legal documents, literature, or when addressing someone we want to show par- ticular respect (e.g., judge). It usually involves addressing someone with the second person plural (called vousvofment). The register courant corre- sponds to the one used in day-to-day life, for in- stance, when we talk to someone new which is typ- ically neutral and includes few grammatical errors. The register familier is the one used with friends, or within a family circle. It usually involves ad- dressing someone with the second singular person fu. It can include a large portion of grammatical errors. Finally, it can also include slang and insults in its most vulgar form. In this work, following what was done in the XFORMAL work (Briakou et al., 2021b), we classify generated text into two classes Soutenu is associated with the formal class familier and courant with the informal class.
encode
The following are other considerations of for- mality in Bengali: (i) Texts containing high fre- quency of Sanskrit-originated words can be consid- ered formal. Aggluinattion/Compunn words can be considered more formal compared to their ana- lytical or elaborated forms. For instance, the words शशरक (formal) / शाशा शाशा (informal)- death have the same meaning, but a different formality (Panda, 1992; Nagarajan, 2014; Ghosh et al., 2022). (ii) Bengali pronouns agree with the verb in lev- cls of formality and there are formal and informal variations of the same verb (David, 2015; Sultana, 2016). For instance, verbs like Give, Eat, Go can be written as सा. श,ड रह (formal) or (R, शा, या (informal) depending on the context. (iii) Among Bengali speakers in Bangladesh, regional dialects
Spanish Formality in Spanish is commonly de- scribed by the T-V distinctions in the singular

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second-person pronoun derived from Latin. Specif- ically, there are two possible translations for the English pronoun "you": zá considered informal, and usted is formal. Both pronouns have different conjugations. The formality in sentences that use the singular second person is easily recognizable, In the case of the other pronouns, the first person is often considered less polite than the third one (Stewart, 2001). For that reason, the third person is commonly used in scientific texts (Salazar et al., 2013). Aside from the pronouns and their conju- gations, according to Cépeda and Tavera (2007), a formal text in Spanish should accomplish other characteristics such as: (i) Having no typographical grammatical errors. (ii) Being set of sentences referring to the same topic. (iii) Being arranged in paragraphs and having a coherent correlation between ideas using appropriate connectors.
been done for low-resource languages like Bengali. To our knowledge, the evaluation of multilingual models for measuring cultural biases like formality has not been attempted so far.
Formality Analysis Previous work in formal- ity analysis has focused on formality classifica- tion (Heylighen and Dewaele, 1999; Abu Sheikha and Inkpen, 2010; Pavlick and Tetreault, 2016; Dementieva et al., 2022), formality style transfer in English (Rao and Tetreault, 2018; Wang et al., 2019, 2020; Czeresnia Etinger and Black, 2019; Madaan et al., 2020; Yao and Yu, 2021: Briakou et al., 2021a), and in the multilingual setting (Ko- rotkova et al., 2019; Briakou et al., 2021b; Krishna et al., 2022). tion to control the generation of machine translation models to target formality has received attention in recent years (Sennrich et al., 2016; Niu et al.. 2017; Feely et al., 2019; Viswanathan et al., 2020; Niu and Carpuat, 2020; Schioppa et al., 2021) and benehmark MT datasets and models have been pub- lished (Nadejde al., 2022; Rippeth et al., 2022).
permallit-sensiiiiie machine transla-
In our work, we check the presence of slang or offensive terms in sequence to classify text as in- formal. Then, T/V distinction in sentences written using the second person defines the formality level. In similar way, sentences written in the third per- son have a bigger probability of being classified as formal compared to the ones written in the first person. The final priority is the layout: paragraph- structured sequences are considered formal in more scenarios than conversationallstrretreee ones.
Recently, several datasets with formality annota- tions have been introduced in English (Lahiri, 2015; Pavlick and Tetreault, 2016; Rao and Tetreault, 2018). XFORMAL (Briakou et al., 2021b), TAOCD (Zaidan and Callison-Burch, 2011) and InFormal (Krishna et al., 2022) extended formality style transfer to the multilingual setting. In the fol- lowing sections, we describe our experiments and results for different languages.
3 Related Work
Biases of Generative LMs Recent literature Large Language Models (LLMs) demonstrated so- cial bias and prejudice against minorities (Sheng et al., 2021; Blodgett et al., 2020; Bender et al., 2021; Bommasani ct al., 2021; Liang et al., 2021) in terms of many categories including gender (Sun et al., 2019; Cao and Daumé III, 2020; Felkner et al., 2022), race (Davidson et al., 2019), religion (Abid et al.. 2021; Malik et al., 2022), occupation, polities and disabilities which result in the pro- duction of damaging content. To create multilin- gual bias evaluation frameworks, it has been argued that careful curation of culturally aware datasets is needed (Talat et al., 2022).
Experiments
We evaluate different dimensions of formality of the generations of two popular generative multilin- gual language models: XGLM (Lin al., 2021b) and BLOOM (Scao et al., 2022), in five languages: Arabic, Bengali, English, Spanish, and French. We hypothesize that the influence of high-resource lan- guages in the corpus can involve biases in the for- mality of the whole models. To see their behavior in different scenarios, we employ distinct variations of prompt lengths and formality. In addition, we tweak some parameters when generating avoid incohesive outputs.
Many papers have focused on measuring social biases and stereotypes against historically disadvan- taged groups and counteracting them for limited number of languages like English (Nadeem al.. 2021; Nangia et al., 2020; Barikeri et al., 2021; Smith et al., 2022), French (Névéol et al., 2022), Hindi (Malik et al., 2022), but similar work has not
(Lin et al., 2021b) is multilingual lan- guage model trained with 500 billion tokens belong- ing to 30 languages of Common Crawl. XGLM has five sizes ranging from 564 million to 7.5 billion parameters.
XGLM

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BLOOM (Scao et al., 2022) is also multilingual generative language model trained on around 341 billion tokens from a corpus of 59 languages (13 of them are programming ones) to democratize huge pre-trained language models. BLOOM was released in different sizes ranging from 560 million to 176 billion parameters.
put. The sources of the prompts include formal- ity datasets such as GYAFC (Rao and Tetreault, 2018), XFORMAL (Briakou et al., 2021b), InFor- mal (Krishna et al., 2022). We also include datasets crawled from the web (Zaidan and Gallison-Burche 2011; I; Cañcte, 2019) and informal songs (Muñoz, 2018). 8).
XLGM and BLOOM are decoder-only trans- formers pre-trained on similar set of languages with a comparable amount of data. We com- pare checkpoints of similar size (i.e. we compare XGLM 2.9B with BLOOM 3B and XGLM 7.5B and BLOOM 7. 7.) Regarding the proportion and data sources which both models were trained, BLOOM was trained a more varied set of do- mains than XGLM in spite of the XGLM corpus being larger. addition, the BLOOM corpus has more balanced distribution of the amount of data of the languages evaluated this study. More details about the quantity and sources of both models can be found in Appendix C.
Table 2 presents the details of which words/group of words we use as short prompts, and the dataset sources of the formal/informal prompts for each language.
Neutral
Formal
Informal
La (When/Then), تعم
TAOCD TAOCD
(Yes), منا (There). (Zaidan
(Zaidan
3 (Unless), /
and
and
(If). من (From), wic (At/When), و swear). في (In). 2011)
Callison- Burch, Burch, 2011)
Callison-
(No)
bn
बाम (I). D (His/Her). xfо (tf).
InFormal (Krishna qरi (h), $ (Whar), et al., dataset 2022)
InFormal
Microblog
(अक (Why), GT (He/She), शायय (OK), Ray (But). काता (They)
(Chowd-
4.1 Prompting for Formality Evaluation
hury and
Chowd-
We employ two prompting strategies to condition the generation of the models. In that way, the be- havior of the model in different scenarios can be assessed.
hury.
2014)
en
The, I, This, He She. You. They, We, Do. There
GYAFC
GYAFC
(Rao and
(Rao and
Tetreault, 2018)
Tetreault,
2018)
Short Neutral Prompts A short prompt is com- posed of up to three words to condition the lan- guage of the output without giving any context that could impact the formality level. That allows us to measure the models' tendency to produce a certain formality level with a neutral input. For the lexicon of each language, we pick set of common words (or combination of them to avoid the confusion of languages when generating) that can be used in both formal and informal sentences.
fr
C'est (It is), Ils (They), Elles (They), I1 (He), elle (She), ce (This), Est-ce que (question).
XFORMAL XFORMAL (Briakou et al., et al., 2021b)
(Briakou
2021b)
Ça (That), Ce (This).
Deux (Two) Por la (For the), Las (The), Los (The), Por el (For the), Con unos (With some), Por que la (Why the), Se ha (It had), Por su (Because of). Para un (For a). De una (0f a)
es
Wikipedia (Cañete, 2019)
9322 rap lyrics in Spanish (filtered) (Muñoz, 2018)
Long Informal/Formal Prompts This set of prompts composed of truncated sentences ex- tracted from existing formal/informal sources. Us- ing these prompts, we can verify how much the models preserve the formality level of their in-
Table 2: Prompts used in experiments. List of the short prompts across the 5 languages. 10 prompts per language are used for 10 generations sampled for each prompt. *Sources of the formal/informal prompts. 100 prompts per language are sampled from these datasets.
WWe use the checkpoints and implementations from https: //wuggiggfacce...mm/ co/models
4.2 Generation Parameters
"httpp//corpus.. rae. es/1 es/1frecuenccaa httlecuuuniasshml,,
Decoding parameters are essential because they can directly affect a language model's output. For each language, we select set parameters to pro- duce fluent text that can be appropriately evaluated. wtt.r/-oooooooooccoeoeee french-words -reendhhoordd---ruue------obbuuu All selections were chosen to impact the formality
1 100- ree-bastc-brrggl--vvccaalaaaeeee
in_English
1000-most-ooeo mo0-moot-common- common-

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Model/Language
Arabic
Bengali
English
French
Spanish
XGLM((.9B)
9.3%
8.0%
6.7%
16.0%
6.7%
BLOOM(3B)
13.3%
4.3%
3.3%
12.0%
3.3%
XGLM(7.5B)
8.7%
5.0%
10.0%
18.0%
7.7%
BLOOM(7.1B)
12.3%
6.3%
3.7%
8.7% *
2.7%
Table 3: % incohesive samples out of the 1200 generated samples each language (300 samples per model). Percentages are averaged across prompt types (400 neutral, 400 formal, and 400 informal prompts). Bolded values show that the corresponding model is significantly better according to permutation-bnn based statistical test with a p-value <5%
level of models as little as possible. This subsec- tion presents our list of generation parameters to reproduce experiments.
guage classified all 1200 generated sequences indi- vidually. We opted for this evaluation procedure be- cause, the time of performing the experiments, to our knowledge, there ity classifier models that included Arabic, Bengali, English, Spanish, and French. To avoid possible bi- ases, each generated output was annotated without looking at its prompt and in a randomized order. All annotations were done by the authors them- selves who are native speakers of the languages. To validate the quality of our annotation process, also collect ratings (collecting the annotations from 2 extra annotators) per sample for 50 samples and report the observed inter-annotator agreement in the Appendix in Table We find the inter- annotator agreement to be above 59% for all the languages reported.
multilingual formal-
Global generation parameters We modified the decoding procedures to avoid very short sentences, code snippets, and outputs in other languages to produce an assessable amount of outputs with significant length to be evaluated. The parameters are listed in Appendix A.
Regarding the total number of evaluated outputs, we generated three sets for each evaluated model and language: 100 with short prompts, 100 with formal prompts, and 100 with informal prompts. That resulted in 1200 generated outputs for each language.
Language-specific generation parameters Be- fore generating the sequences for formality evalu- ation, we tweaked some logit parameters for each language. All modifications were done to obtain more fluent sequences and reduce incohesive out- puts such ones with generation repetitions or con-understandable text. This process was done with a varied set of prompts regardless of length and formality level.
The classification categories for all languages are formal, informal, and incohesive. A sequence is classified as formal informal according to the rules of each language described in section 2. The "Incohesive" label only assigned under certain conditions, such as sequences written in other lan- guages, non- unn-understaadable text (with c.g. many repeated symbol/blank tokens) that cannot be eval- uated for formality level, or code snippets.
We use sampling to obtain the generation out- puts for both models. Three specific parameters were set for both models: We set top-k & to 50, which truncates the number of tokens to sample from. We set high top-p (Holtzman et al., 2019) generate diverse sampled tokens by cumulative frequency, and a high lemperature (Ackley et al., 1985), which does not skew the distribution towards high proba- bility tokens. The specific details of the parameters to reproduce our experiments can be found in Ap- pendix A.
5 Results & Analysis
We start by analyzing the cohesiveness of each model. We then focus on the cohesive text for analyzing formality.
5.1 Cohesiveness of Generation
As seen in Table 3, BLOOM(7.1 (B)) generates signif- icantly more cohesive texts than XGLM(7.5BB) for English, French, and Spanish with p-values under 5%. of a permutation-ball Interestingly, the results Table 3 also show that a larger model does not necessarily lead to more cohesive generations. For example, BLOOM(3B)
statistical test.
4.3 Formality Evaluation
We assessed the formality all generated outputs. To do so, one native/proficient speaker of each lan-

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Model/Language
Arabic
Bengali
English
French
Spanish
XGLM((2.9B)
92%
-3%
14%
41%
58%
BL.OOM(3B)
100%
-6%
-6%
-1 1%
79%
XGLM(7.5BB)
83 %
33%
8%
32%
45%
BLOOM(7.1B)
100%
-3%*
-13%
14%
67%
Table 4: % differences between formal and informal predictions (400 samples per language) sampled with neutral prompts. Light Gray indicates bias toward formal generations and Dark Gray indicates bias toward informal generations. Bolded values show that the corresponding model is significantly better (i.c. closer to 0) than the other model of the same size (based on a permutation- based test with p-value <5%).
generates more cohesive texts than BLOOM(7.IB) for Bengali and English. XGLM(2.99 also gen- erates more cohesive texts than XGLM(7.5B) for English, French, and Spanish. Is worth noting that we are only evaluating cohesiveness in a binary way (cohesive vs. incohesive) and are not judging the quality of the predictions beyond that.
generations with a difference of 41%. For English, XGLM and BLOOM both show small bias (in terms of percentages) towards different directions. XGLM(229BB) and XGLM(7.5BB) show bias towards formal generations by 14% and 8% respectively. However, BLOOM(3B) and BLOOM(7.1B dis- play bias towards informal generations by 6% and 13% respectively. After a careful review of the predictions, we find that French and English in- formal predictions of BLOOM due large proportion of informal generated dialogs.
Besides, the percentage of incohesive texts is noticeably higher for some languages than others for both BLOOM and XGLM. For example, the highest percentage of incohesive texts the case of Bengali, English, and Spanish is less than or equal to 10%, while that percentage is higher in the case of Arabic and French.
BLOOM, this time for Spanish, shows extreme bias towards the formal generations with dif- ference of 79% for BLOOM(3)) and 67% for BLOOMM(.11B). On the other hand, XGLM ex- hibits less bias towards formal generations with a difference of 58% for XGLM((.9B) and 45% for XGLM(7.5BB) These values indicate that both mod- els are influenced by formal sources. In fact, most of the generated sequences with short prompts have the style of news and Wikipedia articles.
5.2 Fore Bias
Neutral prompts, given to an assumingly unbiased model, should lead to equal distributions of formal and informal generations with a difference close to zero between both generations. However, this is not the case here, shown in Table 4. In the case of Bengali. we see that XGLM(2.9B), BLOOM(3B). and BLOOMM(.11B) are almost neutral with mi- nor differences -3% -6% and -3%, respectively, showing bias toward informal generations. On the other hand, we see XGLM(7.5BB) surprisingly, showing significantly more bias toward formal gen- erations than BLOOM(7.1B) with difference of 33%. Upon qualitative analysis, we found that many of the generations of XGLM((75BB) had Ben- gali religious Islamic text-like attributes that were considered formal during annotation, and the usage hashtags or emojis was also less than the smaller model for neutral prompts.
A biased distribution of outputs could be rea- soned by the data the model was trained on. As stated in BLOOM (Scao et al., 2022), the biggest part of the corpus for Arabic was the Arabie- focused Masader repository (Alyafeai et al., 2021; Altaher et al., 2022), which is dominated by Mod- ern Standard Arabic (MSA) that is considered for- mal (cf. section 2). This explains the extreme bias BLOOM(3B) and BLO0M(7.IBB show to- wards formal generations with bias of 100%. XGLM(7.5B) similarly shows extreme bias to- ward formal generations, but significantly less than BLO0M(7.1BB) with difference of 83%.
BLOOM, for French, continues to show less bias showing only a bias of 1% toward informal genera- tions in the case of BLOOM(3B) and 14% towards formal generations in the case of BLOOMM(7.1B) On the other hand, XGLM(2.9B) shows signifi- cantly more bias than (LB)M(3B) toward formal
In terms of model size, XGLM(2.9B) shows more bias towards formal informal generations than XGLM(7.5BB) for all the languages except Bengali, which could indicate that the bigger the XGLM model's 's size, the less bi- ased it is. On the other hand, this isn' 't the case for
notice that

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Arabic F- /I 1-%
Bengali
English
French
Spanish
ModelJ.anguage
F- % 11-1%
F-FF% /I 1-x1%
F-FE% % /I 11-1%
F-FF% / -1%
XGLM(2.9B)
89.4% 61.1%
79.8% / 100.0%*
34.0% /94.0%
26.7% 59.5%
85.9% / 80.2%
BLOOM(3B)
94.2% / 55.1%
83.7% / 187.1%
29.2%
/91.7%
32.0% / 82.0%
77.8% / 90.4%
XGIM(77.5B)
88.6% / 76.7 %
75.5% / 98.8%
34.4%
88.7%%
54.0% / 75.6%
86.9% / 75.8%
BL.OMM(7.1B)
93.5% 51.1%
74.0% 91.9%
27.6% 19.0%%*
25.8% 66.7%
83.8% / 96.8 %
Table 5: Formality preservation samples' percentages for Formal Informal prompts (800 prompts per language: 400 formal and 400 informal). Each sample is annotated as either formal, informal, or incohesive and the percentages are calculated without incohesive text counts. Bolded values show that the corresponding model is significantly better according to a permutation- statistical test with a p-value 55%.
BLOOM as BLOOM(3B) is only expressing more bias for Bengali and Spanish, while BLOOM(7.1B) shows more bias for English and French.
Both BLOOM and XGLM, this time for English, do not preserve the formal style of the prompts for more than 34.4% of the samples for any model. However, they both preserve the informal style in least 84.7% of the generated samples with BLOOM(7.1B preserving significantly more sam- ples than XGLM(7.5B). A similar trend follows for French with both BLOOM and XGLM un- able to preserve the formal style for more than 32.0% of the samples in the case of XGLM(2.9B), BLOOM(3B) and BLOOM(7.1B), On the other hand, XGLM(7.5) preserves the formal style signif- icantly better than BLOOM with a percent- age of 54.0% And again the informal style is being preserved better with, specifically, BLOOM(3B) which preserves the style better than XGLM(2.9B) with a percentage of 82%.
In summary, the models show moderate bias for some languages such as English and Bengali, ex- cept for XGLM(7.5B) ) in the case of Bengali, while also showing extreme bias for other languages such Arabic, French, and Spanish. This difference might be caused by the fact that every language is present in the data with different percentage and is coming from different sources as shown in Ta- ble 8. Overall, it is noticeable that the bias is mostly toward formal generations for all the models and for all the languages.
5.3 Lereel Preservation
In this experiment, we measure how well the for- mality of a generation is the same as the formality level of the prompt (i.e. how well the model pre- serves the formality-level of the prompt). We find that the formality style of the prompts is preserved efficiently for some languages by some models while being almost ignored in some other cases.
The formal and informal styles in Spanish are preserved consistently across the models at least 77.8% of the samples with formal prompts and at least 75.8% with informal prompts with BLOOM IB) preserving the style in significantly more samples than XGLM(7.5BB.
In terms of model size, we notice that the size of the model is not an indicator of how well the model can preserve the formality style. For exam- ple, BLOOM(3B) preserves the formal style better than BLOOM(7. IB) for all languages except Span- ish. In summary, we see that the informal style is mostly preserved well for most languages ex- cept with BLOOM for Arabic. The formal style, on the other hand, is mostly preserved well for all languages except English and French.
For Arabic, as we show in Table 5, BLOOM(3B) and BLOOM(7. ( preserve the formality style of 94.2% and 93.5%, respectively, of the samples when the given prompt is formal. We note that despite being highly biased toward formal text in Arabic (as seen in section 5.2), both models are able to preserve the style of informal prompts, at least 51.1% % of the time. XGLM((.99B), for Bengali, preserves the style of the informal prompts of significantly more sam- ples than BLOOM(3B) with percentage of 100%. BLOOM pays attention to the informal style of the prompts as well, unlike the case for Arabic, and preserves the style of 87.1 % of the samples gener- ated with BLOOM(3B) and 91.9% of the samples generated with BLOOM(7.1B))
5.4 Typographic and Lexical Differences between Formal/Informal Generations
We report Table general statistics about the generated texts of each model and language by formality level. Results show that BLOOM gener-

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ates about twice longer texts as XGLM. In terms of the average number of sentences per genera- tion, BLOOM, when the generation is informal, generates more and shorter sentences than when the generation is formal. Also, informal genera- tions tend to have emojis as expected, especially in the case of Bengali. Besides, informal gener- ations tend to have more punctuation marks than formal ones. Finally, the results of the average number of new lines and the average number of "-", which are used to signal dialogues, support what we mentioned earlier about BLOOM's tendency to generate conversational text.
Finally, the increase in the number of multilingual language models calls for more work on their bi- ases.
Conclusion
In conclusion, we analyzed the formality level of the generations of two large-scale generative lan- guage models, XGLM and BLOOM, ranging from 2B parameters 7B parameters. We first observed the cohesiveness of the predictions. We found that BLOOM(7.1B) predicts significantly more cohe- sive text than XGLM for English, French, and Spanish. Second, we showed that, across all five languages, both models tend to generate formal text when prompted neutrally. Finally, we found that the formality of the prompt highly impacts both models. In most cases, they generate the same style as the prompt, with slight differences between the models depending on the language. Our analy- sis is based on the annotations of I, 1,200 generations in Arabic, Bengali, English, French, and Spanish. We release them with this paper opening future avenues for modeling the formality of generative multilingual language models.
6 Discussion
Formality bias when present in multilingual mod- els, which are increasingly popular nowadays, can lead to undesirable outcomes. For example, using "please" is common among North American En- glish native speakers in requests, even among close friends, while in Arabic, it could be considered awkward, if not rude, in conversations among close friends (Hovy and Yang, 2021). A usage example of language models is solving downstream tasks using prompting techniques for zero-shot learning, such as (Zhong et al., 2021)'s work on question- answering. Prompting has also been used with large language models for conversational chatbots such as ChatGPT (Ouyang et al., 2022). As prompt- ing is becoming popular, we must understand that prompting model that exhibits formality bias could be barrier to getting the expected output. Furthermore, depending on the application, formal- ity bias could even lead sometimes unwanted misunderstandings (Hershcovich et al., 2022) and conflicts the models, for example, are not able to generate text in the formality style of the users' expectations.
8 Limits
In this work, we experiment only with two models, XGLM and BLOOM. The limitation of computing resources tied us with models that have 7.5B param- eters or less and the limitation of financial resources and manpower held us back from experimenting with more languages. Furthermore, the generated samples of each language were annotated by only one annotator, except for English. To validate our methodology
asked two extra native speakers of each language to annotate 50 samples per lan- guage. We share the observed inter-annotator agree- ment values in Table 7 (Gwet, 2014). We find the observed agreement to be above 59% for all the reported languages, supporting the quality of our annotation process. Finally, we note that despite the aforementioned limitations, the methodology used in this study can be applied to any generative system and language.
Controlling LLMS generations has been taken into consideration in recent work, such in (Ouyang et al., 2022), where they fine-tune a lan- guage model (Brown et al., 2020a) intending to align the model with the intent of the users us- ing reinforcement learning from human feedback (RLHF) (Christiano et al., 2017; Stiennon et al., 2020). Future work could analyze the impact of RLHF on the formality distributions present in lan- guage models. Furthermore, our work focused only on two pre-trained models with up to 7B param- eters. The same analysis could be conducted for larger models such as GPT-3 and (100M
Acknowledgment
We thank the Fatima Fellowship and Hugging Face for organizing and sponsoring the Fatima Re- search Fellowship program.
ccf. http:://wwwwaatiffllllleeeee

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---

A Generation parameters
B Descriptive statistics of the generations
We list here the decoding parameters for both mod- els:
General statistics of the generations are in Table 9 reported per language for each model and gener- ation label pair. The table contains the following statistics: the average length of the generation, the average number of sentences in a generation, the average length of the sentences, the average num- ber of emojis per generation, the average number of punctuation marks per generation, the average number of new lines per generation, and finally, the average number of the dialogue mark/dash (-) per generation.
1. We filter out generation sequences that are not natural language (i.c., code) by excluding from the generation process all the tokens that contain any of the following symbols: { ). 1.6.1)1 <,>, and; n..
2. We force the model to generate at least 30 subword tokens (excluding the prompt) to have long enough generation sequence and be able to assess formality.
C XGLM and BLOOM training corpora
We show in Table 8 details of the languages used in our analysis the training corpus BLOOM and XGLM.
3. We a maximum of 150 new tokens of gen- cration to avoid long outputs that could in- clude multiple formality variations.
D Formality Distribution
4. Length the prompts. For the short-prompt setting, we employ at most three tokens to con- dition the generation in the desired language. For the formal/informal prompts, we use 15 words (based white-space tokenization) average.
We visualize the annotated data for each language help in seeing an overview of all the results. Each language is represented by a plot, see Fig- ures 1, 2, 3, 4, and 5, with 12 bars with 3 bars corresponding to each model representing the 3 prompts types: formal informal, and neutral. Each bar in the plot represents 100 texts generated with the corresponding model when prompted with the corresponding prompt type. The colors in each bar represent the possible annotations: formal, informal, and incohesive.
5. Table 6 shows the language-specific genera- tion parameters we used for both BLOOM and XGLM.
Top-k
Top-p
Temperature
Arabic
0.95
Formal Prompt
Formi Informal Incohesive
Informal Prompt
Bengali
50
0.95
Neutral Prompt
English
0.95
|
French
50
0.8
XGLM(2.9B]
Spanish
50
0.8
XGLM(7.58)
Table 6: Language-speciffi generation parameters for both models
BLOOM[38)
BLOOM(7:18)
Observed Agreement
Arabic
0.62
Bengali 0.62
Figure 1: Plot of the distribution of the generations for
English
0.79
Arabic, for each prompt type, according to their labeled categories: Formal, Informal, and Incohesive. Each bar in the plot represents 100 generations.
Spanish
0.59
Table 7: The inter-annotator agreement (IAA) values per language. The values, representing the observed agreement, are based annotations, sample of data, from different native annotators of cach language for 50 random samples.

---

Corpus Size GiB (%)
Data souree domains
XGLM
BLOOM
XGLM
BLOOM
64.34* (0.88%)
ar
~2 upsampled
69.71 (4.34%)
Web
Web, news, books, subtitles, Wikipedia, wikisources
11.19* (0.15%)
bn
17.32 (1.15%)
Web
Web. Wikipedia, Wikisource, open-source NIP datasets Papers. Web, patents, books, subtitles. forums, Wikipedia,
upsampled
3,324.45 (45.66%)
451.64 (30.04%)
Web
fr
303.76 (4.17%)
193.94 (12.90%)
Web
Weh, scholarly documents from all academic fields (HAL), Wik- isource, Wikipedia, subtitles Web, subtitles, Wikipedia, news. magazines
es
363.83 (4.99%)
163.07 (10.84%)
Web
Table 8: XGLM (Lin et al., 2b) and BLOOM (Scao et al., 2022) quantity and data sources in pre-training corpus. XGLM used upsampling for some languages including Arabic and Bengali.
Bengali
French
Pormal Prompt Intormal Frompt
Formal Informal
Formal Prompt
Formal
Intermal Prompt
Informll Incohasiee
Neutral Prompt
Ircchesive
Nextral hrmppt
XGLM(2.9B)
XGLM(2.9B)
XGLM(7.58)
BLODM(3B)
BL00M(7.18)
BL00M(7.18)
Figure 2: Plot of the distribution of the generations for Bengali, for each prompt type, according to their labeled categories: Formal, Informal, and Incohesive. Each bar in the plot represents 100 generations.
Figure 4: Plot of the distribution of the generations for French, for each prompt type, according to their labeled categories: Formal, Informal, and Incohesive. Each bar in the plot represents 100 generations.
English
Spanish
Prompt Neutral Prompt
Farmal Informal Incchesive
Prompt Promot
Forai Infomma Incohesive
Neutral Prompt
XGLM(2.985 98)
XGLM(2.98)
XGLM(7.5B)
XGLM(7.58)
BLD0M[38)
BLDOM[38]
BLOOM(7.18)
BLOOM(7.18)
Figure 3: Plot of the distribution of the generations for English, for each prompt type, according to labeled categories: Formal, Informal, and Incohesive. Each bar in the plot represents 100 generations.
Figure 5: Plot of the distribution of the generations for Spanish, for each prompt type, according their labeled categories: Formal, Informal, and Incohesive. Each bar in the plot represents 100 generations.

---

Aug. Teegh
Aug
Ang. Fieght
Ang pow.teeteiooodd
Ang. Aufiliigge
Infermal
1.354
130,993
C180
6776
0.00
0.000
XGLA (4560) Fermal
246896
1,99
148.993
4230
0.000
0.000
())
Farnsal
(91
5.93
132.993
0.00
6-th)
1656
0315
ADLM(TA
Informal
181345
1471
125023
5:90
0006
0100
Fermal
24400
1M6391
1,29
0,00
0100
REOOMITT ()
Inforeed
041 535
3,00
165
6:00
1:09
500
0.08
2.155 Farmul
206125
186.26
C1RU
238B
254
0334
Phezps
Striiic
Are. Lengts
Avg. #o sEEL prr ad
Ang. Leeets am
Avp. oleeec pergee.
od pacttain surbs an.
Aeg. andd pergaes.
App # dhillet rurtt)
buforss!
Fafarmal
415.355
2:125
195387
0.12
11.007
1.50
0.000
Farnsal
364353
296909
0014
5.56
1.88
0.007
167110
6350
15559
0.000
0.000
Fermal
152297
122.99
0366
22891
0.00
0.00
Informal
419-903
13:044
2009
0.13
Exyli
Progpa
XOLM/B-0) - Tefermal
225-20
5.322
67367
Caee
7.516
0.005
0.000
XGL Peessll
261329
5.103
$3.564
6
5,945
0.008
0.000
Fermal
646.55
6re0
12:537
2:399
9.000
XOLMITAEE
Ju/snns!!
241.85
5.877
68.500
6402
5,001
0.008
0.006
XCEMF
0.000
Informal
S75.399
52.753
ciee
22.374
7.304
1.24
B200N:2-400 Foressl
606.95
6.35
$3J00
CICT
14:33
2050
0.10
Frzach
Prezpi
Stadd
Aig. Lengts
Jug. ool iest pas
Avg. Lengli vas.
Avy. a coago por ges.
Arg. 1 pustation gos
Aeg. sev lece
Arg. Pol dualggc makk))
XGLM/380
Pienal
251/55
2,53
$6/66
6.00
6.961
0.000
0.000
Inferval
6C1.21
11,800
$1417
63Me
25.352
$213
L151
Fermal
612727
6126
69.197
0.00
13.009
2309
0.001
XOLMIT.SP Teformal
206 567
2.890
13,505
9:325
0.000
0.000
XGLME AP)
Porval
235.277
2:911
18:255
6100
1,41
0.088
0.00
(100MEY11Z) Jnyornal
0.00
21897
10.83
2401
Spasih
ADLM(EAE) - Pnformal
222/79
2:756
18374
5.14
0.05
0.00
YOLM Fienal
16
0.000
61M
0.000
0.000
(RR Tsfortl
$51.5
9:91
5.000
51.80
5137
BLODM (B8)
Farmal
015.527
136822
12.922
1734
9.12
(TAN) Jnfermal
22:454
2991
GAIV
5253
9.555
0.000
REPOMITT () Poforual
5.599
18546
C.00
N5555
5
131
22000
Forsss
6AU345
4051
136355
12393
1833
0012
Table 9: The average statistics per formal and informal generation of sequence length, number of sentences, length sentences, number of emojis, number of punctuation marks, number of new lines, and number of the dialogue mark ("-").


Reviews:
[{"review_id": "yqNCSVSyNo", "reviewer": "Reviewer_emAM", "summary": "", "questions": "Questions are posed in point 2 of reasons to reject.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive review and insightful comments. \n\nWe agree with your feedback that collecting three ratings per sample on a subset of the corpus would ensure the quality of our annotation process. This project was done in a resource-constrained setting, and we could not onboard more annotators. Following your suggestion, we will collect two more ratings for at least 50 samples. We will include the inter-annotator agreement on this subset in our paper. \n\nRegarding your second point, our starting assumption (or a priori hypothesis) is that a language model used at scale should not be biased toward any level of formality. It is an assumption on the expected behavior of a “good” language model with regard to formality However, as we've noted in lines 521-533, we find that this hypothesis is disproved by our experiments. We explain the behavior by linking it to the pretraining data distribution. We will rephrase our assumption in line 475 to clarify the argumentation.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. First formality study of LLM output in multilingual setting.\n2. Interesting qualitative and quantitative analysis of LLM output in Section 5, especially in Table 4.", "reasons_to_reject": "1. No discussion or analysis of inter-annotator agreement, which is understandable given that each output was only annotated by one native speaker. While it would be hard to do analysis over the entire corpus, it would be instructive to choose a subset of the corpus (10-50 outputs) and ask 2-3 native speakers to give formality ratings, followed by agreement statistics. Again, this might not change the results very much, but it would validate them, and individual annotator comments might be interesting in of themselves.\n\n2. I'm not sure I agree with 5.2, where the authors suggest in the beginning that neutral prompts should lead to 50-50 distribution of formality. Shouldn't it lead to the distribution of formality in the pretraining corpus? No way to know for sure, but the sources for multilingual data might give an indication. Is it mostly online data (informal) or literature (probably formal)? In fact, I wonder if that is what Table 3 is showing. The authors note this in lines 521-533: perhaps a detailed analysis of the pretrained corpus and a comparison with the annotations would be interesting to add?", "questions_to_authors": "Questions are posed in point 2 of reasons to reject.", "paper_topic_and_main_contributions": "The author's study the biases of generates text from two LLM's for formality across 5 languages. They discover some differences between the two models, as well as between languages which are interesting and worth further investigation. Furthermore, they release an annotated dataset of LLM generated output, which will be useful to the community at large towards studying formality in LLM output.", "excitement": 3, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "7pBj1R4EFU", "reviewer": "Reviewer_13h5", "summary": "", "questions": "Isn’t the “incohesive” label conflating nonsense generations and, e.g., outputs that are just too short to tell the formality level? Maybe it would make more sense to choose a more neutral term such as “unknown”, or differentiate the two cases?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive review and insightful comments. \n\nAbout your point on defining informality: after a careful literature review and discussions with linguists, our take for this work was that creating a consistent definition of informality was not feasible given the language- and culture-specific aspect of formality. We, therefore, chose to define formality carefully for each language independently. We agree that the mention of deep formality in English and the different standard registers in French requires more detailed explanations. We will improve Section 2 based on your feedback.  \n\nRegarding the formality of generations given neutral vs. formal prompts. The percentages in Table 3 and Table 4 cannot be compared directly. The percentages in Table 3 represent the difference between the percentage of formal and informal outputs. On the other hand, the percentages in Table 4 represent, in the case of F->F, the percentages of formal outputs. However, upon a closer look, we indeed find that for English and French, neutral prompts lead to more formal outputs compared to formal prompt predictions (44%  to 30% for English, and 51% to 31% for French, on average). The formal output proportions for Arabic are more balanced between neutral and formal prompts. Finally, for Spanish and Bengali, formal prompts result in more formal outputs compared to neutral prompts (respectively 85% to 70% on average for Spanish and 75% to 50% for Bengali)\nOur interpretation is that this phenomenon is happening in English and French due to the distribution of formal/informal text in the pretraining data and the domains of the formal prompts. Indeed, we noticed that a lot of  neutral prompts result in formal text that follows unambiguously an encyclopedic style and topics, most likely due to pretraining on Wikipedia data. In comparison, the formal prompts used for French and English come from the XFORMAL dataset and are based on reformulated Yahoo answers. Even though formal, these prompts seem not to encode sufficient markers of formality to lead to formal outputs. We will discuss this in more detail in the paper. Thank you for your feedback on this. \n\nAnswering your question about incohesiveness: as described in Appendix C, we force the model to generate at least 30 new subword tokens (excluding the prompt) to have a long enough generation sequence and be able to assess formality. The information provided l.446 is a mistake on our side, and we will fix it. The incohesive category only includes generations that are nonsense.\n\nThank you for the typos and suggestions on formulation. We will also update the colors to be colorblind-safe.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- This is an interesting aspect of multilingual models and cross-lingual differences!\n- There is a clear effort to operationalise formality levels, and to explain conditions in each language.\n- The setup and evaluation is well-described, and the generated & annotated samples are released.", "reasons_to_reject": "- The definitions of “informality” are not really consistent across languages. I understand that there are differences between languages, but for instance: How is soutenu/courant/familier actually defined? What does “deep formality” mean and why is it only mentioned for English? Is dialogue in English/languages without T/V distinction implicitly considered informal? If so, why?\n- There is little attempt made to explain why the “formal” prompts in many cases (EN, most AR, FR, some ES) yield fewer formal continuations than the “neutral” ones. This looks like a calibration issue to me.", "questions_to_authors": "Isn’t the “incohesive” label conflating nonsense generations and, e.g., outputs that are just too short to tell the formality level? Maybe it would make more sense to choose a more neutral term such as “unknown”, or differentiate the two cases?", "paper_topic_and_main_contributions": "This evaluates the generations of two multilingual generative models for their level of formality. Constructing “neutral” prompts and using a selection of formal and informal text datasets, it evaluates to what extent models generate formal text “by default” in different languages, and whether they are able to “preserve” the given formality level.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- l.158 German is listed as a Romance language\n- l.389 you mean Appendix A, right?\n- terminology: be careful not to confuse slang with grammar errors\n- I would suggest using different cell background colours than green and pink, both because these somehow imply that more formal language is “better”, and because I doubt these are colourblind-safe."}}, {"review_id": "DNfEUnv0Am", "reviewer": "Reviewer_4rv1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive review and helpful comments!\n\nWe agree with your feedback that collecting at least 3 ratings per sample would ensure the quality of our annotation process. This project was done in a resource constraints setting, and we were not able to onboard more annotators. Following your suggestion, we will collect 2 more ratings for at least 50 samples that we will add to the next version of our paper. We will also report demographics information of the raters.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Experiments are well-designed to check clearly specified variables. The results are based on a reasonable number of experiments. \nDiagnostics of formality are particular to each language, which is linguistically correct.", "reasons_to_reject": "Annotation of results would be more reliable if it had been done by more than 2 annotators for each language. The authors should have provided a profile of the annotators (age, education, social class). In fact, annotation has been performed in a rather lean way. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "Τhe authors prompt two large language models (LLMs), namely XGLM and BLOOM, with neutral, formal and informal strings in five languages and evaluate their responses for cohesion, formality and informality. They use neutral prompts and non-neutral ones: the latter are pieces of text previously characterised as +/-formal.  The authors also check the impact of (i) the model's size and (ii) the type of texts on which the model has been trained on  the statistics of the type of prompt-type of response relation. The authors base their results on well-planned, substantial experiments. ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Presentation and language satisfactory.\n\n655. which-->who"}, "extra_text": {"Missing_References": "I would expect a more detailed approach to annotation. Possible references:\nhttps://aclanthology.org/2023.acl-long.697/ What’s the Meaning of Superhuman Performance in Today’s NLU? ACL 2023 \nhttps://aclanthology.org/P14-2083.pdf Linguistically debatable or just plain wrong? ACL 2014\n"}}]

Author rebuttal:

Meta-review: This work is a novel contribution to studying the formality of LLMs' outputs in a multilingual setting. The authors provide an in-depth analysis of formality for five different languages and highlight the differences, which is important for characterizing and evaluating the formality of the model outputs. However, I agree with reviewer 13h5 that the definitions of informality are not clear and consistent across languages, which can potentially affect the evaluation of formality (and possibly the reproducibility of the work). This weakness is further emphasized by the fact that there is only one native speaker annotating the non-English outputs (as pointed out by reviewers 4rv1 and emAM).

Based on my personal reading of the paper, the reviewers' comments, and the post-rebuttal discussion, I believe that the setup is clear and well-designed to check the cohesion and formality variables. However, after AC-reviewers discussion, I think that the current version of the paper lacks some important in-depth analysis of the models' behaviors for more generalizable findings. For instance, how does the formality of text distributed in the pretraining corpora affect the formality-level bias and formality-level preservation? The paper briefly touches on this question (e.g., lines 521-533), but a careful empirical study is needed to determine whether the (aggregated) formality of text present in the pretraining corpora is a reasonably good predictor of the formality bias and preservation for any language. There must be (a few) causal factors that explain the contrasting behaviors across different model sizes and different languages. I don't think the current version of the paper can help answer "For a new language, what kind of outputs could we reasonably expect if we use a formal/neutral/informal prompt?" (in other words, generalizability of findings.)
Decision: Accept-Findings