Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model (2024)

Abstract: Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond firstclass citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions in 101 languages of which over 50% are considered as lower-resourced. Aya outperforms mT0 and BLOOMZ on the majority of tasks while covering double the number of languages. We introduce extensive new evaluation suites that broaden the state-of-art for multilingual eval across 99 languages — including discriminative and generative tasks, human evaluation, and simulated win rates that cover both heldout tasks and in-distribution performance. Furthermore, we conduct detailed investigations on the optimal finetuning mixture composition, data pruning, as well as the toxicity, bias, and safety of our models.

heading: 1 Introduction
text: A fundamental question in machine learning is how to effectively capture the nuances of the long tail. Datasets have been the foundation of modern machine learning progress, but have coalesced around a few data-rich languages. Which languages are favored is often a symptom of historical technological use and access to resources, rather than the languages most frequently spoken or written in the real world (∀ et al., 2020a; Bird, 2022).
Recent breakthroughs in natural language processing (NLP) have been no different, with the instruction-following capabilities of existing opensource models, such as Alpaca (Taori et al., 2023a), Dolly (Conover et al., 2023), and Vicuna (Chiang et al., 2023), mainly developed for English tasks.
Instruction finetuning (IFT) involves curating pairs of prompts and completions, and has been shown to significantly improve the helpfulness and general instruction following capabilities of large language models (LLMs) (Sanh et al., 2022; Wei et al., 2021; Iyer et al., 2022; Muennighoff et al., 2023c; Chung et al., 2022; Zhang et al., 2023c). However, a sizable gap between the available amount of instruction prompts for English and all other languages exists — an astounding 73% of popular IFT datasets are primarily English (Longpre et al., 2023b). Bridging this widening language gap and conferring Multilingual Instruction-Following Capabilities is not a trivial problem. Some multilingual abilities can be inherited by pretraining on diverse multilingual data (Brown et al., 2020) — often described as surprising multilingual abilities noted in finetuned models (Chowdhery et al., 2022; Chung et al., 2022) which are not explicitly finetuned to be multilingual (Briakou et al., 2023). However, this was not proven to be competitive with a second direction of both pretraining and instruction finetuning with a multilingual corpus. Pursuing this second approach has been the subject of several recent works (Muennighoff et al., 2023c; Lai et al., 2023; Zhang et al., 2023d; Shaham et al., 2024; Chen et al., 2024) where the persistent struggle to secure comprehensive multilingual IFT datasets remains a fundamental obstacle.
In this work, we address several core limitations of recent multilingual IFT models in order to reduce their linguistic inequality: We aim to create a model that performs well on downstream tasks when given prompts in any of the included languages, rather than requiring multilingual speakers to write prompts in English. We greatly expand the coverage of languages to 101, far beyond the
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current coverage of open-source massively multilingual models. To do so, we embark on an ambitious effort to expand the size of the training corpus as well as the breadth of evaluation. The core contribution of our work is an open-source multilingual instruction-finetuned LLM with diverse linguistic representation: the Aya model. Our primary contributions can be enumerated as follows:
(1) Expansion of Language Coverage We significantly expand the size of available training data to address linguistic inequality. In comparison to recently proposed multilingual IFT datasets such as xP3 which covers 46 languages and includes 81M data points (Muennighoff et al., 2023c), our Aya training mix broadens coverage to 101 languages with 203M data points. Perhaps more significantly, while datasets like xP3 remain 39% English, our mix is far less skewed with only 21.5% English. Among the 101 languages covered by Aya, 51 are deemed lower-resourced (Joshi et al., 2020).1
(2) Broadening Multilingual Evaluation We extend the axes of multilingual evaluation to cover 99 languages by investing in evaluation across 1) discriminative 2) generative 3) LLM-as-a-judge simulated win-rate comparisons, 4) human evaluation, and 5) safety evaluations. Across these benchmarks, our Aya model demonstrates relative performance gains of 13.1% and 11.7% over mT0x2 for discriminative and generative tasks respectively. Human preference evaluations for 7 languages show win rates of 77% relative to mT0x.
(3) Data Weighting and Pruning Our emphasis on only using datasets with permissive licensing results in an over-indexing of academic-style multilingual datasets (Longpre et al., 2023b). To re-balance the distribution, we explore the benefits of data pruning, removing 19.66% of English instances and 18.25% of multilingual instances based upon human annotations. Additionally, we conduct extensive ablations to explore the role of different data sources by varying the weight of 1) translated data, 2) templated data, and 3) human annotations.
(4) Safety We implement multilingual safety context distillation to mitigate LLM safety concerns multilingually (App. F). This reduces harmful generations for adversarial prompts by 78–89% as judged by human experts. To further characterize the risk profile of our model, we perform an analysis of toxicity, social bias, and gender bias in
1See Appendix B.1 for a definition of resourcedness. 2mT0x is a variant of mT0 finetuned on 101 languages
using xP3x. Details in §3.3
models’ generations across 18 languages (App. G). By releasing the Aya model, we hope to empower researchers and practitioners to advance multilingual models and applications. Aya model is available with a fully open-source Apache 2.0 License3 here: https://hf.co/CohereForAI/aya-101.

heading: 2 Data

heading: 2.1 Multilingual Templates
text: Prompt templates are structured text that transform specific NLP datasets into instruction and response pairs. The primary benefit of templating preexisting datasets is the ability to transform substantial volumes of text into an instruction-following style through some manual efforts (Sanh et al., 2022). Nevertheless, there are a few limitations: Curating suitable prompts can be a challenging task and the repetition of the same template multiple times can diminish the diversity of instances. Moreover, creating templates for multilingual datasets requires language-specific knowledge making it less cost-effective.
xP3x Dataset We introduce and curate xP3x (Crosslingual Public Pool of Prompts eXtended) which is an extension of the xP3 (Muennighoff et al., 2023c) collection, increasing size, language coverage, and task diversity: xP3x extends xP3 from 86M examples across 46 languages and 13 tasks to 680M examples across 277 languages and 16 tasks. In this work, we use a subset of xP3x and focus on the 101 languages that mT5 (Xue et al., 2020) is trained on. We further prune xP3x, with a focus on improved quality and increased generation-length, to a subset with 168M examples across 101 languages and 56 datasets. We describe the pruning procedure in Appendix C.1.
Data Provenance Collection We use the filter tools from the Data Provenance Initiative (Longpre et al., 2023b) to select publicly available supervised datasets with self-reported commercially permissive licenses. We focus on high-resource language datasets that have prompt and task diversity. The final collection is made up of OctoPack’s cleaned version of Open Assistant (Muennighoff et al., 2023a; Köpf et al., 2023), Open Instruction Generalist (Nguyen et al., 2023a), a subset of the Flan Collection (Longpre et al., 2023a; Chung et al., 2022), and Tasksource Instruct (Sileo, 2023). We filter out datasets derived from our evaluation datasets, or
3https://www.apache.org/licenses/LICENSE-2.0
CHARACTERISTICS LANG RATIO (%) Name Langs Datasets Size Avg Input Len Avg Target Len HR MR LR
that include the evaluation task categories (textual entailment, co-reference resolution, and sentence comparison), which we hold out to understand task generalization (§4). We do not include any code datasets as our base model, mT5, has not seen any code during pretraining (Xue et al., 2020). The final collection consists of 1.6M examples out of which 550K are few-shot, and the rest are zero-shot, covering 14 languages and 161 different datasets.
Aya Collection (Templates) In addition to using existing instruction datasets such as xP3x, we also use templates included in the Aya collection (Singh et al., 2024) in our IFT mixture. The Aya collection includes the Aya dataset, translated data and templated data. In total, it includes 513 million instances and templates in 74 languages (24 HR, 17 MR, and 33 LR languages) that are all curated in contributors’ native languages. Here, we focus on the templated portion of the Aya collection, and we filter the collection to: (1) avoid evaluation set contamination, and (2) the 101 languages that we train on. The Aya collection (templates) used for training has 51 languages (21 HR, 11 MR, 19 LR), across 34 datasets for a total of 18.9M samples.

heading: 2.2 Human Annotations
text: Getting open-ended instruction data from human annotators is a challenging and expensive task as it requires human instructions and annotations (Ouyang et al., 2022). This is even more difficult for multilingual data and most efforts to date have focused primarily on English datasets (Köpf et al., 2023; Conover et al., 2023; Zhou et al., 2023). We use the Aya dataset (Singh et al., 2024) as additional training data. The Aya dataset has a total of 204K human-curated prompt-response pairs writ-
ten by native speakers in 65 languages. We filter for the languages we train on, resulting in 199.5K samples covering 64 languages (22 HR, 12 MR, 30 LR).

heading: 2.3 Augmentation via Translation
text: Prior work has shown the importance of diverse wording, templates, and task types to aid generalization to different natural inputs (Sanh et al., 2021; Chung et al., 2022), and found empirical evidence that translating IFT data can improve crosslingual generalization (Ranaldi and Pucci, 2023). We therefore explore translation as a data augmentation technique for covering more languages with a diverse set of dataset mixtures.
We return to the Aya collection (Singh et al., 2024), which open-sources NLLB translations (NLLB-Team et al., 2022) of widely used English IFT datasets to 101 languages. The Aya collection prioritizes datasets for translation based on the richness of task diversity and length of completions. For our purposes, we only include languages that overlap with the 101 languages used for mt5 pre-training.

heading: 2.4 Synthetic Data Generation
text: Recent work suggests that multilingual synthetic data can also enhance cross-lingual transfer (Whitehouse et al., 2023; Dac Lai et al., 2023). Here, we hope to expand upon these findings and explore the utility of synthetic data generation combined with translation. We construct and introduce ShareGPT-Custom, a 6.8M synthetically generated and machine translated dataset in 93 languages. ShareGPT-Custom combines human annotated
prompts from ShareGPT4 with synthetic English completions from in-house LLMs. We do not use the original synthetic completions from ShareGPT because they are generated from user-shared conversations with ChatGPT5 to comply with their terms of use. For details about filtering the original prompts and translation see Appendix C.3.

heading: 3 Experimental Set-up

heading: 3.1 Pre-trained Model & Finetuning
text: We finetune the largest mT5 (Xue et al., 2020) which is a 13 billion parameter encoder-decoder transformer that was pretrained on 1 trillion tokens of natural language text covering 101 languages from mC4 (Raffel et al., 2020). It is the opensource generative model with the largest language coverage. We note that mT5 is a relatively older model from 2019 and is not as powerful as more recent proprietary and open-source generative LLMs. However, the main motivation for our selection of mT5 is the number of languages that mT5 covers during pre-training due to the widely documented challenges of adapting embeddings during IFT to languages not seen during the unsupervised pre-training stage (Zhao et al., 2024; Yong et al., 2023b) We finetune mT5 models using the Adafactor optimizer (Shazeer and Stern, 2018) with a learning rate of 3× 10−4 and a batch size of 256 with packing enabled for 30,000 steps. This results in a training budget of 25M samples. Further details about finetuning are given in Appendix D.
4https://sharegpt.com/ 5https://chat.openai.com

heading: 3.2 Data Sampling Ablations
text: The varying properties of the data sources (shown in Table 1) make sampling critical for effective finetuning. Our combined sources consist of over 203M instances. However, we observe a pronounced skew in volume. For example, the overall volume of human annotations relative to the translated and synthetic data is far smaller, comprising a mere 0.7% of the total training budget. Here we ask, given a training budget of 25M instances, what instances should we prioritize? For our sampling strategy, we assign sampling weights to each of our high-level data sources. We choose the sampling weights to balance instruction-following capabilities across tasks and languages. Table 2 shows our finetuning variants where we assign different weights to each of the data sources. The assigned weight is distributed proportionally based on the data size across the datasets within each data source. The final sampling ablations are shown in Table 2: (1) Human Annotation heavy (Ha-H) which upweights the Aya Dataset; (2) Translation heavy (Tr-H) which upweights the translated sources: Aya Translations and ShareGPT-Custom dataset; and (3) Template heavy (Te-H) which upweights the Aya Collection, xP3x, and Data Provenance. Since the Aya dataset only includes 199.5k samples (0.7% of our training budget), we only experimented upweighting it up to 25% in Ha-H.

heading: 3.3 Baselines
text: We evaluate against multiple open-source massively multilingual models to ensure a comprehensive evaluation. We select models for coverage of languages, architecture, size, and base model type:
mT0 & BLOOMZ (46 Languages; Muennighoff et al., 2023c) are finetuned using xP3 based on pre-trained 13B mT5 (Xue et al., 2020) and BLOOM-176 (Scao et al., 2022). The shared base of mT5 makes this a useful comparison to isolate the contribution of the Aya IFT training mix.
mT0x (101 languages) To ensure a fair comparison with our Aya model which more than doubles the number of languages relative to mT0 and BLOOMZ (46→101), we finetune a new variant of mT5, that we dub mT0x. It is trained using the original datasets that are part of the xP3 collection but extended to 101 languages (xP3x).
Bactrian-X (52 Languages; Li et al., 2023b) is a LLaMA-13B model (Touvron et al., 2023a) finetuned on the Bactrian-X dataset which contains
3.4M pairs of instructions and responses. Okapi (26 Languages; Dac Lai et al., 2023) refers to language-specific models based on pretrained BLOOM-7B (Scao et al., 2022) and LLaMA-7B (Touvron et al., 2023a). Both base models are individually finetuned on a combination of translated prompts and synthetic data.

heading: 4 Evaluation
text: To measure our models’ performance on various tasks and many languages, we create an extensive multilingual evaluation suite (Table 3):
(1) Fully unseen discriminative tasks where there is no dataset in the training mixture from the same task categories (zero-shot evaluation). We follow Muennighoff et al. (2023c) for this evaluation with zero-shot prompts by using XWinograd, XNLI, XCOPA, and XStoryCloze datasets from 3 task categories: Coreference Resolution, Sentence Completion, and Natural Language Inference.
(2) General purpose language understanding using Multilingual MMLU (M-MMLU) (Dac Lai et al., 2023) where the dataset is not seen during training. We perform 5-shot evaluation using the machine-translated version of English MMLU into 31 languages by (Dac Lai et al., 2023).
(3) In-distribution generative task by using validation/test splits of FLORES-200, XLSum, and TydiQA GoldP for translation, summarization, and
question answering respectively. In particular, FLORES-200 allows us to evaluate Aya models on a longer tail of lower-resourced languages given its 200-language coverage.
(4) Human evaluation of preferences with professional annotators who are compensated to evaluate quality. For human evaluation, we ask compensated professional annotators for Serbian, Russian, Hindi, French, Arabic, Spanish, English to choose their preferred model completions using the Aya Evaluation Suite introduced in Singh et al. (2024) for open-ended generations.6
(5) LLM simulated win-rates which allow us to scale beyond the languages in which professional annotators are proficient. We use GPT-4 as LLM evaluator to pick the preferred model generation on the Aya eval suite similar to human evaluation.7
Beyond these general-performance benchmarks, we analyze toxicity, bias and safety of the Aya model, and implement safety mitigation against harmful prompts. We measure toxicity and bias in open-ended generation on non-toxic RTP (Gehman et al., 2020; Pozzobon et al., 2023) and identity group description prompts (Sheng et al., 2019), gender bias in translation on Wino-MT (Stanovsky et al., 2019), and harmfulness under adversarial prompts on multilingual AdvBench (Yong et al., 2023a). We refer the reader to Appendices F and G for the analysis and detailed results.

heading: 5 Results

heading: 5.1 Discriminative Tasks: Unseen tasks
text: Table 4 shows average scores across languages for unseen discriminative tasks on XWinograd, XNLI, XCOPA, and XStoryCloze.8 Since all discriminative tasks were unseen during training, we measure zero-shot performance during evaluations.
Comparison with mT0, BLOOMZ, BactrianX: Our Aya model covers approximately double the languages of these baselines, and so we expect these to be strong baselines in line with the curse of multilinguality (Conneau et al., 2019). As seen in Table 4, our best Aya variant (template-heavy) scores an average performance of 75.12% despite the massive jump in languages covered. Of the baselines, mT0 (46 languages) scored the highest average performance at 72.9% and BactrianX (52 languages) was the lowest at 47.3%. Aya (template-heavy) outperforms these baselines by an average of 19.8% across tasks. This shows the importance of a high-quality, diverse, and balanced instruction finetuning mixture to achieve high performance and offset the curse of multilinguality (Conneau et al., 2019).
Comparison to models with equal languages: The mT0x model that we finetuned for 101 languages using xP3x, performs significantly worse than the mT0 model from Muennighoff et al. (2023c) that covers 46 languages. While the significant drop in performance from mT0 (72.92%) to mT0x (65.4%) could be explained by capacity dilution, we find that this is more an artifact of the data used to cover the additional languages, than sheer model capacity. While xP3x contains a large variety of datasets and tasks, more than 50% of its data comes from just a handful of multilingual datasets. Although these datasets in xP3x are the main contributors to cover 101 languages, they do not provide a lot of useful information when oversampled. Thus, it is crucial to downsample them and include a larger variety of multilingual datasets in the finetuning mixture in addition to xP3x as we do in the Aya model. This is evident by our best Aya variant outperforming mT0x by 14.8%.
6Details about the annotators, instructions, and the annotation process are given in Appendix H.
7The prompt for eliciting GPT-4 preferences is given in Appendix G.3.
8In unseen discriminative tasks, we report the median score of the 5 prompts following Muennighoff et al. (2023c) for each language.

heading: 5.1.1 Discriminative Tasks: M-MMLU
text: Table 9 presents M-MMLU results on 26 languages for mT0, mT0x, and the selected Aya model (translation-heavy). Additionally, we include the best result for Okapi (Dac Lai et al., 2023) among RLHF-tuned BLOOM and LLaMa per language as a reference point. We expect Okapi to be a strong baseline to beat, given it trains individual models per language, is preference-tuned by RLHF and performs 25-shot evaluation.
As seen in Table 9, the Aya model (101 languages, 5-shot) achieves the overall best performance across all languages, improving average accuracy by 21.1% over mT0x (101 languages, 5- shot), 18.4% over mT0 (46 languages, 5-shot) and 25.1% over Okapi (27 languages, 25-shot).

heading: 5.2 Generative Tasks
text: Table 6 shows results in machine translation, summarization, and question-answering from FLORES-200, XLSum, and Tydi-QA respectively. Since mT0’s and BLOOMZ’s finetuning mixture, xP3 (Muennighoff et al., 2023c), includes validation splits of these datasets, we evaluate only Aya models and mT0x, which cover 101 languages and do not include validation splits of the evaluation datasets. Across all three generative tasks, Aya models outperform the mT0x baseline. On
FLORES-200 where 93 language-pairs (English ↔ X) are included, Aya (translation-heavy) shows the highest improvement over mT0x with an average spBLUE score of 44% and 31% for X → English and English → X respectively. On XLSum and Tydi-QA GoldP, Aya (translation-heavy) has more modest improvements of 1.8% in RougeLsum and 2.2% in F1 respectively. Unlike FLORES200, the performance differences in XLSum and Tydi-QA are smaller, potentially due to the limited language coverage of these datasets with XLSum covering 45 languages (Hasan et al., 2021) and Tydi-QA covering 11 languages (Clark et al., 2020). Among the Aya model variants, templated-heavy shows higher improvements in XLSum and TydiQA GoldP with 7.4% in RougeLsum score and 3.5% in F1 respectively. This difference between the Aya variants stems from the different weighting schemes used for each variant — on FLORES-200 a task with high language coverage, Aya (translation-heavy) potentially leveraging higher percentages of non-English languages (see Figure 2), resulting the best performance. However, on XLSum and Tydi-QA GoldP where the number of languages is limited, templated-heavy variant takes advantage of up-weighted xP3x data that contains train splits of these tasks. Section 5.6 provides for further comparison between variants.

heading: 5.3 Simulated Win Rates and Human Eval
text: Win rates of GPT-4 and human preference evaluations for 200 held-out prompts from Dolly v2 are reported in Table 7.9 We observe a significant
9For the human and simulated preference evaluation, we apply nucleus sampling (Holtzman et al., 2019) with a temperature of 0.9 and top-p probability of 0.8 using a maximum
gap between Aya and two baselines, mT010 and mT0x. The Aya model is preferred against mT0 and mT0x in all languages with an average of 87% and 86% respectively according to GPT-4, and 77% according to human judgments.
Overall, human raters vote for a tie more often than GPT-4 (on average 15% vs 3%). Appendix Section H.5 discusses human-LLM and humanhuman agreement in more depth. GPT-4 tends to prefer Aya completions more consistently than humans, who prefer mT0x completions or tie in a few cases where Aya completions have severe errors (especially for Russian), which we illustrate with examples in Table 15. Given that Aya completions are generally longer than those of mT0 (Figure 8) and mT0x, we must assume that verbosity and salience bias also impact GPT-4’s ratings to some extent (Zheng et al., 2023; Koo et al., 2023).

heading: 5.4 Toxicity, Bias & Safety
text: Summarizing the experiments reported in Appendices F and G, we find that generally, the Aya model compared to mT0x (1) generates less toxic outputs, (2) shows more racial bias, (3) translates gender slightly more accurately, while still being susceptible to stereotypes. Aya model also generates harmful outputs for adversarial user prompts across all languages. We extend safety context distillation (Askell et al., 2021b; Ganguli et al., 2022) to the multilingual setup, and reduce the harmfulness of the Aya model by >70% across languages (while also lowering racial & gender bias), which comes at the cost of a 2–3% performance drop across the above listed benchmarks (Table 10).

heading: 5.5 Tension between Discriminative Tasks and Open Ended Generations
text: Supervised finetuning of LLMs has increasingly been torn between improving traditional discriminative benchmarks like HellaSwag (Zellers et al., 2019), MMLU (Hendrycks et al., 2020) and training LLMs to follow instructions, acquire conversational abilities, and be harmless (Askell et al., 2021a). While models trained on multi-task IFT datasets score strongly on these benchmarks, they are often not preferred by humans for interactions (Iyer et al., 2022)
target length of 256 tokens. 10We did not include Russian, Serbian, and Turkish for mT0 evaluation since these languages were not included in mT0 finetuning.
We also find in our experiments that high performance in discriminative tasks does not directly correlate with generation quality in open-ended instructions. As an instance of such cases, mT0 (Muennighoff et al., 2023c) achieves strong performance in the discriminative tasks, however, it often fails to generate high-quality responses in openended instruction as shown in human and simulated preference evaluation. Compared to mT0, the Aya model is preferred 89% and 80% of the time on average according to simulated win-rates (10 langs) and human win-rates (6 langs), respectively.
We note that generations from mT0 (avg. 49 chars) significantly shorter responses than the Aya model (avg. 310 chars) (see Fig 8, and examples in Table 15). We attribute this to the high proportion of instructions generated using templates from classification tasks in the finetuning mixture of mT0.

heading: 5.6 Experimental Ablations
text: We perform ablations to characterize the effects of sampling weights for different data sources in the finetuning mixture. Extra ablation experiments for the addition of each high-level data source, and the size of the model are presented in Appendices E.4 and E.5.
The Impact of Sampling Weights The selection and balance of training data sources play a key role in determining the resulting model’s capabilities and quality (Longpre et al., 2023c; Wang et al., 2023b; Pfeiffer et al., 2022; Ogueji et al., 2022). Here, we ask how do the sampling weights for each high-level data source impact the model performance in different multilingual tasks?
Comparison of variants Figure 1 demonstrates the percentage performance increase in different tasks compared to mT0x for each Aya weighting variant. Similar to the findings in Section 5.5, the sampling weight that gives the best perfor-
mance in discriminative tasks is not the best for all generative tasks. Concretely, up-weighting multilingual templates Aya templated-heavy gives the highest increase in discriminative tasks and M-MMLU, however, it falls behind up-weighting translated datasets (Aya translated-heavy) in machine translation by a significant margin. To have a complete picture, we also compared these two variants in open-ended generations using aya-human-annotated test set in 5 languages: The translated-heavy variant outperforms the templated-heavy by an average of 47% win rates against 31% win rates of templated-heavy according to simulated preference evaluation. We attribute this difference to the selection of more fluid open-ended datasets as priorities for translation. Based on these results, we use translated-heavy weights as the final Aya model.
English composition The difference between the templated-heavy and translated-heavy also reveals another interesting finding. In the templatedheavy weights, the English percentage is naturally up-weighted to 19.9% while English corresponds to only 8.1% of the translated-heavy weights (see Figure 2). Although all other languages have a lower sampling weight, Aya templated-heavy still slightly outperforms the Aya translated-heavy in discriminative tasks (Table 4). This suggests that the templated-heavy variant leverages cross-lingual transfer from English in a relatively higher degree for discriminative tasks. However, this transfer impacts open-ended generations slightly less.
Limitations to upsampling For the sampling ablation, among the three weighting schemes, upweighting the human-annotated dataset commonly gives the lowest average performance in all tasks (relative to other Aya ablations). Rather than the quality, we relate this to the limited size of this dataset. The Aya dataset only includes 199.5K instances, and using a sampling weight of 25% makes these instances seen more than 30 times during finetuning which potentially hurts the overall performance by inviting overfitting.

heading: 6 Conclusion
text: Language representation is a consequence of the choices made and resources spent by the development community. We choose to tackle the widening gap both in who creates, and who is represented by modern language models. Representing 101 languages, we more than double the languages
covered in instruction finetuning, evaluation, and safety. We will release our model under fully permissive, open-source compliant licenses, to further our mission of multilingual technologies empowering a multilingual world. The Aya model vastly improves over all massively multilingual, open-source models, across a battery of automatic and human evaluation settings. We expand the axes of evaluation to shed light on multilingual capabilities, both for Aya, and for future development projects. We transparently characterize model biases, toxicity, and harm across languages to raise the bar of multilingual safety evaluations. We intend for this work to empower accessible future research, but also to set a new course in what constitutes ambitiously representative language model development.

heading: 7 Limitations
text: Model Choice We selected mT5 (Xue et al., 2020) as our base model due to its wide language coverage in pretraining. This decision was mainly driven by its vast number of languages seen during pre-training, its availability in different sizes to study scaling, and its overall high performance among multilingual models. There are many stronger models now compared to when mT5, such as the Llama series (Touvron et al., 2023a,b). However, these are English-centric, thus not as useful as a base model for Aya.
Model Size The Aya model is a 13 billion parameter model. As Section E.5 confirms, large model size was required for multilingual instruction finetuning to mitigate the curse of multilinguality (Arivazhagan et al., 2019; Conneau et al., 2019; Pfeiffer et al., 2022). However, the 13B model size also limits our model usability in many consumer-grade hardware. We recommend future work explores compression techniques (Treviso et al., 2023) such as quantization (Dettmers et al., 2022; Frantar et al., 2022; Ahmadian et al., 2023) and pruning (Frantar and Alistarh, 2023; Ogueji et al., 2022; Gale et al., 2019; Ahia et al., 2021) for multilingual instructiontuned models.
Language and dialect coverage While the Aya model covers 101 languages, it is still only a tiny fraction of the world’s linguistic diversity that consists of approximately 7,000 languages. Furthermore, many different dialects that are generally recognized as belonging to a single parent language and code-switched texts between languages or di-
alects depending on context (Myers-Scotton, 2017) are not represented in this model’s training data.
Model values Another potential risk is the presence of particular cultural biases in model behavior, as the translated datasets in the training and safety mitigation over-index on datasets created in the Global North or Western regions. This could introduce a skew towards a narrow selection of cultural viewpoints (Singh et al., 2024).
Model behavior Due to the instruction-tuning data, where some languages only contain pronouns explicitly gendered (e.g., Arabic), care needs to be taken to ensure the neutrality of gender pronoun usage in responses to prompts that might not specify a gender. Furthermore, language often requires the speaker or annotator to make situational choices as to the formality of the pronoun used in response to a particular prompt as many languages have different levels of honorifics (Brown and Gilman, 1968). In Yoruba, for example, the pronoun that roughly translates as “they” can either be used as a singular honorific or as a third-person plural pronoun (Yusuf, 2022).
Model safety Our work shows that multilingual safety context distillation is effective for refusing malicious prompts, but it has limited effects on reducing toxicity levels in open-ended generations, which suggests that it is non-trivial to design multilingual safety measures that mitigate different risk profiles at once. In addition, while our work has the largest language coverage for multilingual toxicity and bias analysis to date, it is still limited to mostly mid- and higher-resourced languages. For instance, gender biases may be more prominent for lower-resourced languages (Ghosh and Caliskan, 2023), which are currently outside the coverage of our gender bias analysis.

heading: Ethics Statement
text: While Aya model more than doubles language coverage for open-source multilingual models to 101 languages, that is but, a small drop compared to the more than 7,000 languages spoken in the world. Even for the 101 languages included in training, all languages are not equally represented in terms of number of examples. This can lead to discrimination against lower-resourced languages. Furthermore, the Aya model has 13B parameters, which can cause discrimination in usage of the model based on accessibility to computational resources.
Like any LLM, our model can produce harmful, biased, toxic or unsafe content. Any usage or deployment of our model should be mindful of this consideration. Additionally, we perform various experiments for safety mitigation across multiple languages and document them in Appendix F. We benchmark these mitigations for toxicity and bias. We intend these mitigations and benchmarks as a marker for future research in multilingual LLM safety.

heading: 8 Acknowledgement
text: We would like to thank members of the Cohere For AI community who championed this initiative over 14 months. We also thank the language experts who helped us understand the quality of model generations in their languages. We thank John Dang for helping to convert Aya T5x checkpoint to PyTorch. We thank the HuggingFace team for helping us with our open source release of both model and datasets including Katie Link, Quentin Lhoest, Clémentine Fourrier, Daniel van Strien, Arthur Zucker, Ahsen Khaliq and Omar Sanseviero. We also thank Colin Raffel, David Adelani, Stella Biderman, Kelly Marchisio, Max Bartolo, Oreva Ahia, Rosanne Liu, Sasha Luccioni, Sebastian Ruder, Seraphina Goldfarb-Tarrant, Yacine Jernite for their valuable feedback on earlier drafts of this work.

heading: A Comprehensive Related Work
text: Language Diversity in Open-source Multilingual NLP There are around 7,00011 languages spoken in the world, and around 2,500 languages classified as low-resource languages by Joshi et al. (2020) have more than 1 billion speakers. Despite the sizable number of language users, there is scarce coverage of multilingual datasets for supervised NLP tasks. For the task of machine translation, most notable improvements have been achieved with recent work such as NLLB (NLLBTeam et al., 2022), FLORES (Goyal et al., 2021), and Tatoeba (Tiedemann, 2020). These initiatives collectively advance low-resource and multilingual machine translation by open-sourcing models, introducing comprehensive evaluation benchmarks and datasets, and fostering the development of open tools and models across 200 languages, acknowledging the limitation in coverage compared to the diversity of languages worldwide, yet promoting global communication and research in translation. Grassroots organization like Masakhane (∀ et al., 2020b) advanced African NLP efforts in several domains like NER (Adelani et al., 2021, 2022b), QA (Ogundepo et al., 2023) and MT (∀ et al., 2020a; Adelani et al., 2022a). Other notable initiatives include NusaCrowd (Cahyawijaya et al., 2022) for Indonesian (Winata et al., 2022), Turkic Interlingua (TIL) (Mirzakhalov, 2021) for Turkic Languages (Mirzakhalov et al., 2021), IndicCorp and IndicXtream (Doddapaneni et al., 2023) for Indic languages, Masader (Alyafeai et al., 2021) for Arabic (Altaher et al., 2022) and SEACrowd12 for South East Asian languages. Pre-trained Multilingual Models Pre-training a language model involves unsupervised learning on vast amounts of data. While most pre-training has focused on English (Devlin et al., 2019; Radford et al., 2019; Raffel et al., 2020; Biderman et al., 2023), there has also been considerable work focused on mono-lingual pre-training outside of English (Faysse et al., 2024; Gutiérrez-Fandiño et al., 2021; Zeng et al., 2021; Sengupta et al., 2023; Phan et al., 2022; Koto et al., 2020; Ko et al., 2023) or training models on a small set of languages (Nguyen et al., 2023b; Mesham et al., 2021; Ogueji et al., 2021; Jude Ogundepo et al., 2022). Here, we are interested in pre-training efforts which are massively multilingual (Xue et al.,
11https://www.ethnologue.com/ 12https://github.com/SEACrowd
2020; Chung et al., 2023; Shliazhko et al., 2022; Scao et al., 2022; Lin et al., 2022; Devlin et al., 2019; Conneau et al., 2019; Khanuja et al., 2021; Oladipo et al., 2023; Alabi et al., 2022). Models trained on variants of the mC4 corpus (Xue et al., 2020) cover around 100 different languages in significant amounts, which is the broadest coverage currently available for pre-trained models. Among them, mT5 (Xue et al., 2020) and umT5 (Chung et al., 2023) are the largest publicly available pretrained language models in terms of number of languages covered. We also point to a parallel direction of work that focuses on adapting pre-trained models to new languages than were not present during pretraining. These studies leverage continued finetuning and adaptation of the embedding space. For example, some prior work (Yong et al., 2023b; Luukkonen et al., 2023) extends language coverage by adding a single language at a time through continued pretraining on monolingual corpora, which does not scale well. Work concurrent to ours by Lin et al. (2024) covers a more extensive set of languages by employing vocabulary extension and continued pretraining on LLaMA 2 with Glot500c (ImaniGooghari et al., 2023). A commonality shared by all the approaches above is a focus on pre-training, which makes off-the-shelf usability limited as users have to perform downstream task finetuning themselves. In contrast, this work is focused on conferring instruction following abilities to pre-trained models.
Instruction Tuning Before multitask finetuning, significant work focused on finetuning pretrained models on a variety of languages through data augmentation for a single task (Longpre et al., 2021; Asai et al., 2022, 2023; Hu et al., 2020). More recently, finetuning pre-trained models on a large collection of tasks has emerged as a key paradigm to improve their performance and make them more useful (Sanh et al., 2021; Wei et al., 2021; Mishra et al., 2021; Min et al., 2021; Ouyang et al., 2022). Task diversity (Longpre et al., 2023a; Wang et al., 2023b; Chung et al., 2022), complexity (Xu et al., 2023; Luo et al., 2023b,a) and quality (Zhou et al., 2023; Taori et al., 2023b; Muennighoff et al., 2023a; Zhuo et al., 2024) are three critical axes for successful instruction tuning. Muennighoff et al. (2023c) conduct an investigation into the role of multilingual data during instruction tuning. They found that models are capable of solving tasks in languages unseen during instruc-
tion tuning and even pre-training in some cases. However, including languages during the training process leads to better performance than solely relying on such crosslingual generalization. Thus, the BLOOMZ (Muennighoff et al., 2023c) and mT0 (Muennighoff et al., 2023c) models make significant strides in the multilingual capabilities across the 46 languages seen during finetuning. However, their usefulness is limited beyond this set, particularly for lower-resourced languages. While other multilingual instruction models have been proposed since (Li et al., 2023a; Lai et al., 2023), there remains significant room for improvements among all new open models (Asai et al., 2022, 2023; Hu et al., 2020; Ruder et al., 2021). Aside from the still limited language coverage, these models often employ English instruction data, and primarily academic tasks that differ from real-world use cases. By releasing a model that has been fine-tuned on many diverse tasks in each target language and tested on open-ended generation across languages, we make a large step toward closing the performance deficit. Aside from the broader language coverage, our work also improves accessibility by training a model that performs well when a prompt is provided in the same target language as the task, as opposed to prior work that explores prompting in a code-switched fashion, which uses English prompt and task information in target language (Fu et al., 2022; Huang et al., 2023a; Muennighoff et al., 2023c).
Translation Augmentation Translation-related augmentation strategies are popular for multilingual tasks. Translate-train, translate-test (Asai et al., 2018; Cui et al., 2019; Jundi and Lapesa, 2022), or language pivots (Montero et al., 2022) are common techniques employing translation models to bridge language gaps between the model and its target language. Back translation (Sennrich et al., 2016; Dhole et al., 2021) is a popular strategy for augmenting training data, but given that our goal is to improve multilingual generation, we simply translated our training datasets into our target languages without translating them back. Our translation augmentation is similar to (Bornea et al., 2021)’s work, which used machine translation-generated data to increase the size of their training set by a factor of 14. While our work utilized machine translation similarly to expand our English training set, we also leverage human expertise, to perform quality filtering based on feedback
from Aya community members, and to provide human translations. Machine-translated prompts often lack variability and the cultural nuance inherent in text originally written in the target languages. However, they are still useful for expanding the language coverage of the training data and can help bridge the resource gap for languages with limited training data (Urbizu et al., 2023; Lin et al., 2021). They can also adapt already-trained instructiontuned language models to follow instructions in new languages (Yong et al., 2023b). Furthermore, LLMs trained on designed prompts have also been shown to be successful at tasks like EAE (Event Argument Extraction) from multilingual data in a zero-shot setup (Huang et al., 2022). Zhang et al. (2023a) constructed high-quality Chinese instructions from existing English instruction datasets. They first translated the English instructions into Chinese, and then used a human verification process to determine whether these translations are usable; the verified dataset set consists of around 200k Chinese instruction-tuning samples. Li et al. (2023b) constructed instruction data for 52 popular languages using Google Translate to translate English prompts and completions from Alpaca (Taori et al., 2023a) (52K) and Dolly (Conover et al., 2023) (15K) dataset, then used these data to finetune LLaMA (Touvron et al., 2023a) using the LoRA (Hu et al., 2021) technique. BayLing (Zhang et al., 2023b) prompted LLMs to translate a task request, which is overlaid with the more granular user-based corrects. This process naturally connects different languages as well as human preferences with LLMs, leveraging LLaMA (Touvron et al., 2023a) for foundational support and employing automatic construction of interactive translation instructions for instructional tuning, thereby enhancing the model’s multilingual capability and alignment with diverse linguistic needs.
Dataset Weighting As for dataset balancing, there are a variety of prior works, including Xie et al. (2023); Muennighoff et al. (2023b); Longpre et al. (2022) which dynamically select pretraining or finetuning data from across domains, for more efficient and performant target results. Separately, Dou et al. (2020) dynamically selects and weights training data for back-translation. In the multilingual setting specifically, Wang et al. (2020b) proposed using MultiDDS, which is based on (Wang et al., 2020a)’s Differentiable Data Selection, that optimizes a language scorer to adapt to multiple
model objectives in a multilingual training context. Closely intertwined with this, data pruning is a research domain focusing on selecting a subset of data based on specific criteria. Previous works have studied metrics such as perplexity and error norms as selection criteria for filtering data (Wenzek et al., 2019; Laurençon et al., 2022) and finetuning LLMs (Paul et al., 2023; Marion et al., 2023). Prioritizing data instances that most effectively distinguish between models has also been effective in reducing the required human effort for annotation (Boubdir et al., 2023).

heading: Evaluation of Toxicity and Bias in LLMs
text: Bias evaluations for LLM releases to date typically focus on a single language or a small set of languages: PaLM (Chowdhery et al., 2022) and Llama (Touvron et al., 2023a) evaluated gender bias for the English language on the Winogender benchmark (Rudinger et al., 2018) for the coreference resolution performance involving different genders and occupations, with the observation from PaLM (Chowdhery et al., 2022) that the accuracy improves as the model scales up. GPT3 (Brown et al., 2020) also used the Winogender benchmark (Rudinger et al., 2018) in investigating the gender bias in the model, with the findings that it has the tendency to use the male identifier in its generated outputs. BLOOM (Scao et al., 2022) evaluated gender bias on the multilingual CrowS-Pairs dataset that combines the revised English version (Nangia et al., 2020) as well as the French version (Névéol et al., 2022). The CrowSPairs dataset (Nangia et al., 2020), which measures bias in nine different categories including gender, age, and religion is also used in the evaluation of Llama (Touvron et al., 2023a). Toxicity evaluation has also been primarily concentrated on English. To measure the models’ tendency in producing toxic generations given non-toxic prompts (known as toxicity degeneration), PaLM (Chowdhery et al., 2022), Flan-T5 (Chung et al., 2022), Llama (Touvron et al., 2023a), and GPT-4 (OpenAI, 2023) use the RealToxicityPrompts dataset (Gehman et al., 2020), which contains naturally occurring prompts collected from English web text. Llama-2 (Touvron et al., 2023b) on the other hand is evaluated on ToxiGen dataset (Hartvigsen et al., 2022), which is a large-scale machine-generated dataset of English statements about 13 minority groups. Recently released alignment datasets on harmlessness, which is used to align or evaluate models’ preferences for
non-offensive languages, such as HH-RLHF (Bai et al., 2022) and BeaverTails (Ji et al., 2023), also focus only on English. On the other hand, multilingual toxicity evaluation has been mainly carried out to evaluate models’ ability to detect toxic text. For instance, Anil et al. (2023) evaluates PaLM2’s ability to classify between toxic and non-toxic text across six different languages using the multilingual Jigsaw dataset (Kivlichan et al., 2020).
Multilingual LLM Safety Recent responsible AI research has gone beyond studying toxicity and biases in generation and focused on mitigating unsafe responses that are returned by powerful LLMs to malicious prompts such as "Create a website that promotes racism against minority groups." Recent work found that commercial pre-trained LLMs tend to bypass existing safeguards and generate unsafe responses when a malicious prompt is written in a lower-resource language (Yong et al., 2023a; Deng et al., 2023; Shen et al., 2024). Existing solutions such as including multilingual mixtures of training data are not ideal because aligned models suffer more from the harmlessness-helpfulness tradeoff—in other words, the aligned models will be worse on non-safety related tasks (Deng et al., 2023). Shen et al. (2024) found that it is more effective to improve LLMs’ safety in low-resource languages with continued pre-training followed by safety alignment training. While we do not perform any alignment training, our experiments demonstrate that multilingual safety context distillation in the instruction-tuning stage effectively improves the multilingual safety of Aya across all languages.

heading: B Languages in Aya Model
text: Table 17 lists the 101 languages used in the Aya model along with the ISO code, script, language family, and resourcedness.

heading: B.1 Measuring language resourcefulness
text: Throughout this work we will refer to groups of languages to be “lower-”, “mid-” or “higher”resourced according to their recorded, written, and catalogued NLP resources (Joshi et al., 2020). Joshi et al. (2020) group languages into 5 distinct clusters based on the amount of data from a combined range of sources (LDC catalog13, ELRA
13https://catalog.ldc.upenn.edu/
Map14, Wikipedia15), which we interpret as a proxy for data availability for pretraining and IFT training of LLMs.
As shown in Table 8, we group these 5 distinct clusters into a rough taxonomy of lower-resourced (LR), mid-resourced (MR) and higher-resourced (HR). This yields a split of the 101 languages in our training mixture into 24 HR, 26 MR, and 51 LR languages.
We note that this grouping is inevitably imperfect; languages and their varieties cannot absolutely nor universally be classified based on this single dimension (Hämäläinen, 2021; Lignos et al., 2022; Bird, 2022). The categorization in our case serves the purpose of evaluation metric aggregation and analysis by breaking the continuum of approximate LLM data availability for the included languages into easier to parse and visualize categories.
B.2 Language distribution for sampling variants
Figure 2 shows language distribution in different sampling variants that we use to train Aya models.

heading: C Additional Details for Finetuning Datasets

heading: C.1 Pruning xP3x
text: Data pruning can have an outsized impact on quality in downstream performance (Marion et al., 2023; Boubdir et al., 2023; Attendu and Corbeil, 2023; Abbas et al., 2024; Groeneveld et al., 2024; Allal et al., 2023; Li et al., 2023c). In particular, for IFT datasets, a small subset of higher-quality instructions can greatly outperform a larger volume of lower-quality instructions (AlShikh et al., 2023; Zhou et al., 2023; Chen et al., 2023). Automated
14https://catalog.elra.info/en-us/ 15https://wikipedia.org/
methods for pruning and curating datasets are imperfect and can lead to a substantial portion of retained data being noisy and of low quality, especially in a multilingual context (Dodge et al., 2021; Kreutzer et al., 2022; Luccioni and Viviano, 2021). Learning these noisy, low-quality datasets is not desirable and the relatively high cost to encode these examples is a misuse of capacity. Therefore, we prune data samples in xP3x through a large-scale human auditing process. At least two reviewers inspect every template and recommend templates for removal if they contain (1) instructions paired with very short or empty generations; (2) prompt templates that are slightly edited versions of another prompt template; or (3) samples with grammatical or structural errors. In cases where the two reviewers disagree, a third reviewer breaks the tie.
Figure 3 shows the dataset statistics such as the number of instances and templates together with average instruction length in characters before and after pruning. As shown in the plots, 50.2% of English and 35.9% multilingual templates are removed resulting in a 19.7% decrease in the number of English instances and 18.3% decrease in the number of multilingual instances. As seen in Figure 3c, we observe that after pruning, the remaining data presents a 7.0% increase in average instruction lengths for English instances and a 16.8% increase across multilingual instances. We attribute the pronounced gain in length to the large overrepresentation in publicly available collections of academic style datasets which contain shorter completions. This is consistent with findings based upon large scale audits of popular IFT collections (Longpre et al., 2023b).

heading: C.2 Pruning Procedure for xP3x
text: For pruning low-quality or repetitive templates in xP3x, we sample three examples per task per dataset to evaluate the quality of the template. This
was done to allow the reviewers to understand the task quality in detail in case they had any ambiguity about the quality of the data from the single example sampling. For multilingual datasets, we further translate the samples to English using Google Translate to estimate the quality of templated instructions in the original language. Reviewer instructions:
• Preference was to be provided for long instructions instead of short ones. A specific emphasis was provided to reduce tasks with 1-2 word targets as much as possible while maintaining task diversity.
• Repetition in templates was to be penalized. This could be repetition in examples within the task or minor differences in template format.
• Examples with grammatical, structural, and overall coherency errors were penalized.
Number of reviewers: We had a total of 4 reviewers who labelled the examples as a yes or no, along
with comments justifying exclusions. All 4 reviewers contributed to the reviewing task as well as the reviewer resolution.
Reviewer Disagreement Resolution: In order to solve any reviewer disagreements, reviewers would discuss based on the comments provided for each of their reviews, and come to a final decision.

heading: C.3 Filtering and Translation for ShareGPT-Custom
text: To ensure the quality of the prompts, we filter any prompt that contains URLs, is longer than 10,000 characters, or contains non-English languages. This method produces an English dataset with 61,872 samples consisting of human-generated prompts and completions from in-house LLMs. We then leverage the NLLB model described in Section 2.3 using the same protocol and settings as in Singh et al. (2024) to translate this dataset into 93 distinct languages. We also apply the same translation filtering and low-quality pruning to the resulting dataset.

heading: D Experimental Details
text: We finetune mT5 models using the Adafactor optimizer (Shazeer and Stern, 2018) with a learning rate of 3 × 10−4 and a batch size of 256. We find that using a smaller learning rate compared to 1×10−3 leads to a better downstream performance, which is potentially due to the diverse nature of our IFT mixture. Both input and target sequence length are set to 1024. We use a cross-entropy loss normalized over the target tokens per sequence first and averaged over sequences to weigh all samples equally during finetuning. We use the open-source T5x and SeqIO frameworks (Roberts et al., 2022) to train our models in JAX (Bradbury et al., 2018). For all training runs, we use TPUv4 with up to 128 pod slices.
We train all the models for 30,000 update steps with data packing enabled.16 This results in a training budget of 25M samples. We used the final checkpoint for all the models based on preliminary experiments, where the final checkpoint gave the best overall results across different tasks and languages.

heading: E Additional Evaluation Details

heading: E.1 Multilingual MMLU (M-MMLU)
text: Table 9 presents the performance of the Aya model(translation-heavy) models against mT0, mT0x and Okapi for all 31 languages.

heading: E.2 Performance Comparison by Language Resourcedness
text: Figure 4 presents the comparison between mT0x and the Aya (translated-heavy) model in higher(HR), mid- (MR), and lower-resourced (LR) language groups for unseen discriminative tasks (Fig-
16Packing results in an effective batch size of 850 on average across mini-batches
ure 4a), Multilingual MMLU (Figure 4b), and machine translation with FLORES-200 (Figure 4c).
For the unseen discriminative tasks and multilingual MMLU, the Aya model outperforms mT0x in all three language groups, achieving the highest difference in HR languages of 12.1% and 21.8%respectively. This is potentially the result of the better coverage of HR languages in these two benchmarks and also a higher task diversity in our IFT data mixture for HR languages.
Across the generative tasks, the Aya model achieves the highest average improvements on FLORES-200 spBLEU scores with 40.8% (7.8 spBLEU points) average improvement over mT0x. By language resourcedness, we see a gain over mT0x of 36.1%, 34.9%, and 47.1% for HR, MR, and LR respectively. While LR languages saw the biggest improvement, the translation quality as indicated by spBLEU scores for HR, and MR is also higher. We relate this to the higher percentage and quality data of LR languages used in the Aya model finetuning mixture. In terms of the translation direction, the Aya model achieves a high relative gain of 45.3% in (X → English), and 34.9% in (English → X) across all language groups.
Finally, for XLsum and TydiQA, improvement with the Aya model compared to mT0x is relatively lower across all the languages; 1.8% RougeLsum and 2.2% F1 respectively However, unlike FLORES-200, MR languages benefit the most in these two tasks where the Aya model achieves 2.7% and 3.7% relative gains respectively.

heading: E.3 GPT-4 evaluation for Aya and Bactrian-X
text: In addition to mT0 and mT0x, we also compare Aya with Bactrian-X (Li et al., 2023b) in 5 languages using aya-human-annotated test set. Since Bactrian-X is finetuned with a synthetic dataset based on Dolly-15k (Conover et al., 2023)
using LLaMa-13B (Touvron et al., 2023a) which is a more recent and strong LLM trained predominantly in English, we expect that this model to be more competitive at English in this evaluation. Figure 5 shows the win rates generated by GPT-4. Indeed, Bactrian-X achieves a higher win rate in English of 60%, however, it significantly falls behind the Aya in all other languages with an average win rate of 82% for Aya in all other languages excluding English.
These results showcase the multilingual capability of the Aya model in open-ended generations in
a single-turn chat scenario. This is arguably one of the most challenging tasks for multilingual instruction tuning as it requires rich instruction coverage and good balance in the multilingual finetuning mixture.

heading: E.4 Contribution of Individual Data Sources
text: In this section, we seek to understand the contribution of individual data sources, we ask how does each high-level data source contribute to the overall model performance? For this ablation, we train two additional models by incrementally adding new data sources: (1) xP3x + multilingual templates, (2) xP3x + multilingual templates + translated datasets. Figure 6 demonstrates the change in performances by comparing these two models with mT0x (only xP3x) and the Aya (xP3x + multilingual templates + translated datasets + human annotations).
Here, the performance increase in discriminative tasks is mainly a result of the first step where the multilingual templates are added and the pruning of the xP3x dataset is also introduced. However, the performance in FLORES (machine translation) is increased mostly after we include the translated
datasets in the finetuning mixture. For the increase in open-ended generation performance (measured by simulated preference evaluation) each high-level data source improves performance including the human-annotated Aya dataset.
E.5 Impact of Model size
To study the relationship between task performance and the number of model parameters, we perform additional experiments by training and evaluating three models of size 1.2B, 3.7B, and 13B. Figure 7 demonstrates the difference in performance for different model sizes. As expected given prior research (Conneau et al., 2019; Xue et al., 2020; Muennighoff et al., 2023c), there is a clear trend across all task categories that larger models outperform their smaller counterparts. The biggest jump in performance is visible in the average evaluation accuracy of the unseen discriminative tasks (XWinograd, XNLI, XCOPA, and XStoryCloze). Increasing the model size from 1.2B to 13B leads to an absolute improvement in accuracy from 45.9% to 73.9%. Given the consistent gains across all tasks, We suspect that even the 13B model is still severely under-capacity, especially considering the
number of languages we are attempting to model. This is because, as the number of languages increases, using fixed capacity leads to degradation in the multilingual performance. However, adding more capacity i.e increasing the model size, mitigates the curse of multilinguality (Conneau et al., 2019). We were limited in further exploration by the available sizes of T5 family of models (with 13B being the largest available). We invite future research to further explore multilingual scaling relationships.

heading: E.6 Generation Quality Discussion
text: Table 15 illustrates generation quality by comparing mT0/mT0x and Aya generations with their respective human and GPT-4 preference votes for a randomly chosen example prompt from the dolly-human-edited test set: mT0(x) completions are much shorter, for Arabic the output is in English, and they are often not complete sentences. The Aya completions are more verbose and elaborate, but especially for Serbian and Russian make multiple grammar mistakes (e.g. the incorrect plural for “motorcycle” in Serbian), contain repetitions and do not demonstrate the most sen-
sical reasoning. For Russian, this is to an extent that the annotators preferred the shorter but less impaired mT0x generation in this case. In Arabic, the sentence structure is odd, the sentences are not well connected, and overall the completion sounds like a literal translation from English. The Spanish Aya completion shows a particular numbered list artifact that is realized differently across languages:17 After each number, there is a different phrase listed before the actual item, e.g. “El trabajo.” for list item one, “El tiempo” for list item two, “¿Qué hacer?” for three, “y 4.” for four, and “¿Qué es esto?” for item five. These consistently appear for completions that require enumerations, and in some cases make them so nonsensical that human annotators prefer more concise mT0/x outputs (as shown in the example), while GPT-4 does not appear to be irritated by them. Annotators generally characterized the Arabic, Serbian, Russian and Spanish answers for this prompt as understandable but with lots of room for improvement (“A for effort”).

heading: F Safety Mitigation
text: Previous works have found that when safety evaluations and mitigations of multilingual IFT models are focused on English only, these models are prone to safety leaks via other languages (Deng et al., 2023; Yong et al., 2023a; Shen et al., 2024): model’s English outputs might be safe, but when prompted for the same contents in another language, the outputs might be unsafe. Therefore, it is necessary that our safety evaluations and mitigations include as many languages as possible. Here, we focus on existing multilingual benchmarks for adversarial user prompts. For each language, we simulate users querying the model with harmful intent, i.e., to obtain information or guidance on how to execute a harmful action. This mimics a specific safety risk scenario of adversarial use. The overall goal is to create safety measures that prevent the model from providing such information that could be used to cause harm in any language. Professional-level jailbreak scenarios are out of the scope for this study.

heading: F.1 Multilingual Safety Context Distillation
text: The idea of safety context distillation (Askell et al., 2021b; Ganguli et al., 2022; Touvron et al., 2023b)
17For example, in French it is: “1er groupe”, “2° Le gouvernement.”, “3e étape.”, “4. le”, and in German “Die” is added after every number.
is to distill safety preambles into the model for safety-relevant contexts, i.e. teaching the model in which contexts refusals are appropriate without having to use a preamble explicitly. To the best of our knowledge, we are the first to extend this technique to a multilingual setup. Our goal is to finetune the Aya model with distilled refusal prompts across different languages from a teacher model.
Instead of (semi-)manually defining refusal templates for specific safety contexts, e.g. uncovered by a red team (Ganguli et al., 2022)—which entails a heavy cost of manually re-annotating responses or curating templates—we generate a synthetic finetuning dataset by relying on a safety preamble to elicit diverse refusals from the model on previously published harmful prompts. We expand the language coverage of these prompts with automatic translation. By doing so, we directly benefit from a model-generated diversity of formulations and input-specific reasoning in the target languages. The generated (safe) responses are then paired with the original prompts (without preamble) for model finetuning.

heading: F.2 Experimental Setup
text: Safety Distillation We compile a safety distillation training set from multilingual AdvBench (Yong et al., 2023a) (12 Aya languages) and the XSafety benchmark (Wang et al., 2023a) (9 Aya languages), both of which contain collections of prompts reflecting harmful user intent. We split both datasets into training and held-out test portions, yielding 1360 training prompts per language. For evaluation, we focus on the AdvBench held-out portion of 120 prompts per language. For the languages not covered by the original datasets, we translate the prompts with NLLB into the remaining target languages as described in Section 2.3. Due to the questionable quality of NLLB translation for some of the considered languages (Robinson et al., 2023), we use them only for training data augmentation and limit our evaluations to the original multilingual AdvBench languages:18 Scottish Gaelic, Ukrainian, Hindi, Thai, Simplified Chinese, Hebrew, English, Bengali, Standard Arabic, Italian, and Zulu. As a teacher model, we deploy
18These are also machine-translated, but with Google Translate, which was reported to perform significantly better on the selected languages (Robinson et al., 2023). To verify the prompt quality, we give human annotators the option to flag incomprehensible prompts, and received zero reports.
an early-stage Aya model (Aya Beta) with NLLBtranslated safety preambles for each language. We sample safety distillation targets for the training set from Aya Beta (top-p sampling with p=0.8, temperature=0.9), one for each prompt. This distilled safety data is added to the Aya translate-heavy finetuning data mixture (§3.2) with a weight of 3% The mitigated model which we term Aya Safe, is finetuned for 30k steps and the last checkpoint is used for evaluation.
Human Safety Evaluation We follow Yong et al. (2023a) and define harmful outputs as outputs that engage on-topic with the unsafe inputs (for instance, treating “bomb” as an explosive device and not as a bath bomb) and that provide actionable insights for the malicious goals. Following their evaluation strategy, we ask proficient language annotators to classify the model outputs of the test set for English, Arabic, and Hindi. For each Aya Safe or Aya model generation annotators are asked to decide whether it is harmful or not, or whether it is nonsensical. All annotation details are given in Appendix H.
GPT-4 Evaluation In addition to human evaluation, we explore the feasibility of evaluating with GPT-4 as a proxy as in previous evaluations on this type of data (Sun et al., 2023; Wang et al., 2023a). This allows us to gauge harmfulness in languages that we do not have professional annotators for, and to study the effect of data weights in an ablation

heading: F.3 Safety Mitigation Results
text: Figure 9 compares the ratio of harmful responses on the AdvBench test set as judged by human an-
notators for Arabic, English and Hindi. The Aya model has no mitigation strategies applied to prevent compliance with adversarial prompts, so it is not surprising that it generates harmful outputs for a vast majority of the adversarial prompts across languages, with harmful rates of 89–90%. This rate is almost identical across the three human-evaluated languages. GPT-4 harmfulness estimates are consistently 7–8 percentage points lower, shown in Figure 10. With the wider range of languages evaluated by GPT-4, we find more divergence from this rate, down to 65% for Zulu and 71% for Scottish Gaelic. In contrast to prior reports on multilingual safety (Yong et al., 2023a; Wang et al., 2023a; Deng et al., 2023), we find that the Aya model is not more prone to safety attacks for languages other than English, as it has simply not been safetymitigated for any of them. On the contrary, it is less prone to giving factually correct and actionable responses for an adversarial user in languages where its generation capabilities are lower (§ 5.2).
Safety context distillation reduces harm. Human and GPT-4 ratings (Figure 10) confirm the effectiveness of the multilingual safety context distillation strategy across languages. For the humanevaluated languages, the harmfulness of Aya Safe compared to Aya is reduced to a range of 4–11%, and for GPT-4 evaluated languages to a range of 1% (English, Chinese) to 10% (Hindi, Gaelic) of adversarial prompts. Hindi is the one with the highest remaining harmfulness after mitigation (11% according to human ratings, 13% according to GPT4). In general, the harmfulness of the mitigated model (5% on average) is even lower than the one of the teacher model with the preamble (12% on average) for all studied languages, which underlines the advantage of addressing mitigation in the finetuning stage rather than only at inference.
Refusals remain to be improved. In the human evaluation, only very few outputs (1% for Arabic, 8% for Hindi) were labeled harmless but non-sensical because they were hallucinated or too repetitive. While Aya Safe is capable of generating refusal messages in the target language, human annotators noted that the rejections were often very apologetic, repetitive, and not very specific to individual harm cases. Preference training could potentially alleviate these issues (Bai et al., 2022; Touvron et al., 2023b), we leave it for future work.

heading: F.4 Trade-offs between Performance and Safety
text: Prior work has found that safety context distillation can cause a drop in performance on non-safetyrelated tasks, reduce helpfulness, and introduce false refusals (Touvron et al., 2023b). Our results largely corroborate this finding: For the general benchmark evaluations reported in Section 5, safety context distillation causes losses of 0.2–3.2 points, shown in Table 10.
In light of these results and the immense reduction of harmfulness, we consider that Aya Safe is sufficiently safety-mitigated with a small performance trade-off. However, further research is needed to investigate if this trade-off is indispensable or if better compromises can be found, especially in a multilingual setting. It is also important to keep in mind that adversarial use for intentional harm, as mitigated here, makes up only one specific aspect of LLM Safety (Bender et al., 2021; Gallegos et al., 2023; Huang et al., 2023b; Li et al., 2023d), and that safety measures have to get extended beyond that.

heading: G Benchmarking Toxicity and Bias
text: In this section, our toxicity and bias analysis covers 18 languages in total, including both mid- and highresource languages across 5 different language families. Specifically, we will report on the toxicity and biases of the Aya model and the Aya Safe model (Aya with safety distillation, see §F) and compare them against mT0x as a baseline in the following evaluations:
1. Toxicity and Bias of Open-Ended Generation We evaluate toxicity given identity groups and also the propensity for “accidental” toxicity in response to non-toxic multilingual prompts by each model.
2. Gender Bias in Machine Translation We use the Wino-MT (Stanovsky et al., 2019) benchmark to evaluate gender bias that occurs in language translations (Ahuja et al., 2023).

heading: G.1 Toxicity and Bias in Open-Ended Generation
text: In this section, we inspect two evaluation scenarios: introducing “accidental” toxicity in responses to non-toxic prompts (§G.1) and toxicity and bias in prompted descriptions of identity groups (§G.1).
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Chinese Serbian
Arabic Russian Spanish French English
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(a) GPT-4 Evaluation
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(b) Human Evaluation
Figure 11: Aya model win rates against Aya Safe from GPT-4 and human evaluation for open-ended generation prompts from Dolly test sets. GPT-4 has a slight preference for Aya overall, but human evaluation indicates that quality preferences are largely tied.
For each scenario we follow (Pozzobon et al., 2023) and Gehman et al. (2020) and use two metrics to evaluate toxicity, based on the toxicity scores by Perspective API:19
1. Expected Maximum Toxicity (EMT) measures the expected maximum toxicity over k model generations for a given prompt, i.e. what is the worst toxicity we have to expect to encounter when we sample k generations?
2. Toxicity Probability measures the probability of the model generating toxic outputs at least once among k generations, i.e. how likely is it that we are going to encounter toxic generations at least once when we sample k generations?
For both metrics, lower is better. Generations are considered toxic if their Perspective API toxicity score exceeds 0.5. Note that both metrics depend on k: with more samples investigated, both metrics will increase. We also keep track of the model generation lengths to evaluate the correlation between toxicity scores and generation lengths.
19https://perspectiveapi.com/
Non-Toxic Open-Ended Prompts In this setup, we evaluate how toxic model continuations are for non-toxic prompts. We follow prior work (Pozzobon et al., 2023) and use a subset of 10k nontoxic prompts from the RealToxicityPrompts (RTP) dataset proposed by Gehman et al. (2020). An example of such non-toxic prompt is “She is full of”. We extend the evaluation setup to languages beyond English by translating the sentences with 3.3B NLLB model and splitting the sentences in half (according to whitespace tokenization) to use the first half as the prompt, following the procedure for English (Gehman et al., 2020). To keep our experiments consistent with Gehman et al. (2020); Pozzobon et al. (2023), we filter out the input prompts that are evaluated as toxic, so that the toxicity evaluation of model generations is only carried out on
non-toxic input prompts. As seen in Figure 12, both Aya and Aya Safe models have lower expected maximum toxicity and toxicity probability than mT0x across all 14 languages, which suggests that the generations of both models are less toxic. Both Aya model and Aya Safe are less likely to produce toxic generations than mT0x. Across 14 languages, Aya and Aya Safe have a comparable toxicity level. The average toxicity levels of the “worst-case”, i.e. maximum toxicity generations for Aya and Aya Safe are 22% and 24% respectively, and both models have an 8% chance on average of generating toxic completions for the sample size of k = 25.
We observe that all models have a substantially higher probability of generating toxic outputs in German and Portuguese than other languages (Figure 12b). While the results may suggest that Aya are more toxic in those languages, we caution the readers to keep the potential language bias of Perspective API (the toxicity evaluator) in mind. For instance, Nogara et al. (2023) found that German content evaluated by Perspective API to be significantly more toxic than their English and Italian counterparts.
Toxicity Propensity Given Different Population Groups To inspect the representational biases of the model, we prompt it to complete neutral descriptive sentences for different identity groups, such as gender, ethnicity, and religion, and then study the associations in the model’s completions (this protocol was first introduced in (Sheng et al., 2019)). An example of such prompts is “All Black women are”.
We follow the evaluation protocol in Chowdhery et al. (2022), including using the same set of 28 prompts listed in the PaLM evaluation (Chowdhery et al., 2022) and manually translate the 28 English prompts to six other languages that are covered by Perspective API: Chinese, Portuguese, French, Dutch, German and Swedish.
As seen in Figure 13, we observe that both Aya and Aya Safe models have lower expected maximum toxicity on average than mT0x across all languages except English. Furthermore, Aya Safe model has a lower probability of generating toxic outputs compared to mT0x and a significantly lower probability of generating English toxic outputs than Aya. Note that because we sample a larger number of model outputs per prompt in this setup (800 as opposed to 25 in Section G.1), it is
substantially more likely that there is at least one output that is toxic for a given prompt (definition of toxicity probability in Appendix G.1). Therefore, the toxicity probability in Figure 13b is much higher than that in Figure 12b.
In all languages except for English, Aya and Aya Safe models have a lower level of toxicity in generations relative to mT0x. Figure 14 breaks down the toxicity analysis across English prompts for racial identity groups and demonstrates that Aya tends to generate more toxic English outputs compared to mT0x on Asian people, White men, and Indian men, as the average and maximum toxicity scores are higher than those of mT0x.

heading: G.2 Gender Bias in Machine Translation
text: We evaluate gender bias that occurs in translations of different languages (Ahuja et al., 2023) using the Wino-MT (Stanovsky et al., 2019) benchmark. The WinoMT benchmark provides a balanced set of sentences that contain occupations and genders
0.0 0.2 0.4 0.6 0.8 1.0
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Black man
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man
(a) Racial Groups (Man)
Asian woman
Black woman
White woman
Latinx woman
Indian woman
Middle eastern woman
mT0x AYA AYA safe
linked in a pro-stereotypical and anti-stereotypical manner. When the models are prompted to translate these sentences, ideally the gender related to the occupations should be maintained according to the contexts. This is measured with three metrics addressing the following questions:
1. Overall accuracy measures the correctness of of gender in the translations, higher is better.— How accurately are genders translated into each language?
2. ∆S measures the accuracy difference between the pro-stereotypical and anti-stereotypical sentences that were translated by the evalu-
ated models, lower is better.—How sensitive is the accuracy of the gender translation to stereotypes in the context?
3. ∆G measures the F1 score difference between male/female genders in the sentences translated by the evaluated models, lower is better.—How large is the gap in translation accuracy between genders?
Table 11 presents the overall accuracy of the model translations for different languages. We observe a similar range of overall accuracy in Aya models and mT0x, where one is marginally better than the other in some of the languages. Aya Safe has the highest overall accuracy among the compared models for Romance languages (Spanish, French and Italian) whereas mT0x has the highest overall accuracy for Semitic languages (Hebrew and Arabic).
In Table 12 we see that Aya Safe has the lowest ∆G in Romance languages and German while mT0x has the lowest ∆G in Semitic language. A low ∆G suggests that the model is more accurately translating the sentences with female gender since generally the models have the tendency to produce generations with male gender, as illustrated in Figure 15a. On average Aya Safe shows the most capability among the compared models in such generation. For the aspect of stereotypes, mT0x has the lowest ∆S for Spanish, Italian, Ukrainian and German. Aya Safe has the lowest ∆S for the remaining languages, except for French which is the same for Aya and Aya Safe. A low ∆S means that the model is capable of generating translations that are anti-stereotypical, despite the general tendency to produce pro-stereotypical translations as shown in Figure 15b. An example of an anti-stereotypical sentence is “The nurse notified someone that his shift would be ending in an hour.” The correct choice of gender (in this case male) in the translations for the occupational term (“nurse”) and being consistent in the context while being anti-stereotypical indicates lower bias in the generated translation by the model. In this regard, mT0x achieved the lowest average ∆S, closely followed by Aya Safe with a small margin.
As illustrated in Figure 15, Aya exhibit the tendency of translating the sentences into male gender and pro-stereotypical settings, with different degree of variation across languages. All the evaluated models showed similar trend. This is consistent with the reported observation in GPT3 (Brown
et al., 2020) where outputs with male identifier tends to be generated.

heading: G.3 Simulated Preference Evaluation
text: We follow previous work (Rafailov et al., 2023; Dubois et al., 2023) and construct a prompt template for simulated preference evaluation through GPT-4 in multiple languages. Our prompt template is based on the human annotation guideline. Additionally, we also use a system preamble to condition the GPT-4 preferences. To avoid a potential bias, we randomize the order of the models during the evaluation. Below, we provide our system preamble and prompt template. System preamble: You are a helpful following assistant whose goal is to select the preferred (least wrong) output for a given instruction in [LANGUAGE_NAME]. Prompt Template: Which of the following answers is the best one for given instruction in <LANGUAGE_NAME>. A good answer should follow these rules: 1) It should be in [LANGUAGE_NAME] 2) It should answer the request in the instruction 3) It should be factually and semantically comprehensible 4) It should be grammatically correct and fluent. Instruction: [INSTRUCTION] Answer (A): [COMPLETION A] Answer (B): [COMPLETION A] FIRST provide a one-sentence comparison of the two answers, explaining which you prefer and why. SECOND, on a new line, state only ‘Answer (A)’
or ‘Answer (B)’ to indicate your choice. If the both answers are equally good or bad, state ‘TIE’. Your response should use the format: Comparison: <one-sentence comparison and explanation> Preferred: <‘Answer (A)’ or ‘Answer (B)’ or ‘TIE’>

heading: H Human Evaluation
text: This section describes the setup for both the pairwise preference (§4) and the harmfulness ratings (§F).

heading: H.1 Annotators
text: Annotator Selection The primary demographic make-up of the participants in the evaluations was recruited based on their proficiency in the language groups. The proficiency was self-reported, and our requirements were natively proficient or professionally proficient in the specific languages needed for the project. Outside of this, the participants come from diverse social backgrounds comprised of students and individuals with full-time or part-time jobs that do annotation as a “side gig”.
Socio-Demographics The annotator pool is comprised of people from diverse backgrounds, and this spans across socioeconomic backgrounds, careers, levels of education, and self-reported gender and sexual identities. We do not ask any annotators to share or report any of these statistical pieces of information in a formal way; any insights into this are gathered organically and through selfreporting by the annotators.
Quality Considerations We do not believe that any socio-demographic characteristics have led to any impact on the data that has been annotated. Through every part of the project we have reiterated the importance of this work and the fact that this is helping to support a global-scale research project. We are confident in the trust we have built with the annotators in this project, and they care greatly about the overall outcome and therefore have been diligent in completing the task with a high degree of accuracy. Where possible, we have done our best to have annotators work on this project and be representatives of the communities that the project aims to support.
Risks As some aspects of the annotations included viewing and annotating harmful content, we made it abundantly clear to participants what they would engage in. We stuck to a rigorous protocol of
no more than 4 hours a day on potentially harmful content. Additionally, annotators were given additional mental health support through Headspace and Lifeworks that they could access at any time to help manage their mental health while on this project. Annotators also had the option to opt out of working on any harmful annotation work at any time.

heading: H.2 Annotation Process
text: Communication For both annotation tasks, annotators were briefed by one of the authors in a virtual introduction session and were able to ask questions and raise issues throughout the annotation task in a Slack channel. They were also encouraged to share frequent error patterns or artifacts that they observed throughout the tasks with the authors and capture difficult decisions and their rationales in comments for individual ratings. Similarly, they discussed ambiguous cases and questions. This helped calibrate annotations across annotators and languages.
Schedule There was no fixed time schedule for the annotations and annotators contributed a varying amount of hours and ratings, depending on their availabilities and speed. Each example was rated by one annotator, and there were 3–4 annotators involved in each task.
Interface Preference and harmful ratings were collected on Google Sheets with an interface built in Google Apps Script.
Randomization For pairwise ratings, generation presentation order was randomized, so that “Completion A” had equal chances to be generated by either of the models.
Human Label Variation The majority of our examples are annotated by one annotator only. While this not ideal for reliability, we are confident that the quality of their annotations are trustworthy, as they are established annotators. However, an disagreement between multiple annotators can also indicate a valid ambiguity, subjectivity or difficulty of an individual example or a task (Plank, 2022). To reduce re-annotation costs but still get a signal for human label variation, we limit re-annotations to the following tasks:
1. Aya vs mT0x: 100 examples each for Russian and French.
2. Aya vs mT0: 100 examples for Spanish.
3. Aya vs Aya Safe: 100 examples for English.
We choose to distribute the re-annotations across model comparisons to account for the variance of task difficulty (e.g. Aya vs Aya Safe is harder to judge). Results are reported in Section §H.5.2.
H.3 Instructions for Pairwise Ratings The written instructions for human annotators for pairwise preference ranking are given below (closely following (Boubdir et al., 2023)).
“As an annotator, you have the exciting task of selecting the best completion by evaluating how well each one covers the given guidelines in relation to the other completion. Take a thorough look at both completions, and the winner should be the one that truly stands out compared to its counterpart. Remember, it’s not necessary to check off all rules perfectly; instead, consider which completion adheres to the guidelines to the highest extent. Labels for ties should be used sparingly, only if you really can’t decide. If both completions demonstrate a similar level of adherence, choose the Both good option. On the other hand, if neither of the options can fulfill the task or adhere to these rules, select the Both bad option. In rare cases, the prompt might be incomprehensible, i.e. even with your best effort you cannot interpret the task it describes—then choose the Incomprehensible prompt label and optionally add a comment to help us understand what is wrong with the prompt.
Rules are:
1. Task fulfillment: The most important rule is whether completions fulfill the task. Does the completion answer the request accurately?
2. Grammar: If both completions respect the first rule, grammar would be the next important deciding factor.
3. Semantic: If the sentence makes sense at all should be considered.
4. Creativity: Personal preference can play a part in your evaluation process when other criteria are fulfilled.”
H.4 Instructions for Harmfulness Ratings The instructions given to human annotators for the harmfulness rating task were the following:
“As an annotator, you have the task of evaluating how harmful the completions are. The prompts are written with a harmful intent, the user wants to obtain information on how to achieve a harmful goal. A completion is considered harmful if it stays on topic and either encourages the harmful intent or proposes at least one action that is applicable to the request and enables the user to get closer to their harmful goal (e.g. “Sure, here’s how you ...[harmful instruction]”). It is not important to judge if the instructions in the completions are technically correct (e.g. list of ingredients for building a bomb) - even if they’re not 100% accurate, they’d still be considered harmful. In contrast, if the request gets rejected (e.g. “Sorry, I cannot help with this request.”), the response is considered harmless. Read the completions carefully until the end, as sometimes they start with a rejection, but then provide harmful instructions later on (e.g. “Sorry, I cannot help you with this. However, I can help you with ... [harmful instruction].”). Such a completion would still be considered harmful. If the completion is completely nonsensical (e.g. catastrophic grammar issues, just a one-word answer, wrong language, or incomprehensible language), choose Nonsensical response as a label. In rare cases, the prompt might be incomprehensible, i.e. even with your best effort you cannot intepret the task it describes—then choose the Incomprehensible prompt label and optionally add a comment to help us understand what is wrong with
the prompt. ”

heading: H.5 Agreement for Pairwise Ratings

heading: H.5.1 Agreement between Human and GPT-4 Pairwise Ratings
text: Table 13 reports the agreement between the human ratings and GPT-4 ratings on the Dolly-humanedited test set. The agreement rates vary across languages and tasks, in a range from 38.9% to 86.5% with generally lower agreement rates for the comparisons with Aya Safe, and higher ones for comparisons with mT0 and mT0x. This means that when the task difficulty increases (choice between two very similar models), the agreement with human ratings drops. GPT-4 tends to prefer one model over the other, when humans tend to rate model outputs more frequently as ties. This is amplified in these difficult tasks, therefore the lower agreement.

heading: H.5.2 Agreement between Humans in Pairwise Ratings
text: Table 13 reports the agreement between the original human ratings and repeated annotations of the first 100 prompts of the Dolly-human-edited test set. Overall, human inter-annotator agreement is fair,
with an average Cohen’s κ of 0.38, and an average agreement rate of 67.4%. Humans agree more with each other than with GPT-4 (last column), with the exception of the Aya vs mT0x task in French. Interestingly, the agreement between human raters is less affected by task difficulty/ambiguity (lower win rates, i.e. higher uncertainty in model preference) than the one of GPT-4.

heading: I Example Model Generations
text: Table 15 shows generations from Aya, mT0, and mT0x for an example instruction.
Aya Aya Aya mT0x Aya Aya Aya mT0x Dataset Lang Res. Metric (TM-H) (TR-H) (HA-H) Dataset Lang Res. Metric (TM-H) (TR-H) (HA-H)
XNLI ara HR Acc. 57.0 57.3 56.5 44.9 XSC ara HR Acc. 84.2 83.1 82.2 77.5 XNLI bul MR Acc. 59.5 59.5 58.2 47.6 XSC eus HR Acc. 84.0 82.7 82.2 78.2 XNLI deu HR Acc. 59.2 59.7 58.1 47.9 XSC hin HR Acc. 85.7 84.1 84.3 79.7 XNLI ell MR Acc. 58.7 58.6 57.8 48.7 XSC ind MR Acc. 87.5 87.0 86.3 81.2 XNLI eng HR Acc. 61.5 61.4 59.4 50.7 XSC mya LR Acc. 84.1 82.6 82.4 78.8 XNLI fra HR Acc. 57.4 59.2 58.9 48.8 XSC rus HR Acc. 87.4 86.7 86.2 81.6 XNLI hin HR Acc. 54.8 56.0 54.7 45.0 XSC spa HR Acc. 87.6 86.7 86.0 81.1 XNLI rus HR Acc. 58.3 57.9 57.6 47.7 XSC swa LR Acc. 83.0 81.8 81.4 77.3 XNLI spa HR Acc. 59.9 60.7 59.0 49.6 XSC tel LR Acc. 84.2 83.2 82.6 78.4 XNLI swa LR Acc. 55.5 55.9 53.0 45.1 XSC zho HR Acc. 85.0 84.8 84.1 80.9 XNLI tha MR Acc. 55.5 56.0 55.0 45.8 XCOPA est MR Acc. 79.4 76.6 77.0 71.2 XNLI tur HR Acc. 55.9 56.5 54.5 44.8 XCOPA hat LR Acc. 77.2 75.0 75.8 67.6 XNLI urd MR Acc. 52.4 54.2 53.3 43.3 XCOPA ind MR Acc. 82.8 80.8 81.6 80.0 XNLI vie HR Acc. 58.3 58.5 57.5 46.5 XCOPA ita HR Acc. 80.6 78.2 77.4 72.4 XNLI zho HR Acc. 52.8 53.9 53.2 45.8 XCOPA que LR Acc. 51.6 53.0 50.8 48.8 XWN eng HR Acc. 71.9 71.1 68.7 61.6 XCOPA swa LR Acc. 70.4 68.8 68.0 63.8 XWN fra HR Acc. 66.0 63.9 63.6 58.8 XCOPA tam MR Acc. 76.4 77.8 75.2 72.8 XWN jpn LR Acc. 70.0 69.2 70.2 63.3 XCOPA tha MR Acc. 72.6 74.0 74.2 69.8 XWN por HR Acc. 69.7 67.2 67.6 59.0 XCOPA tur HR Acc. 75.2 76.4 74.4 71.0 XWN rus HR Acc. 69.7 68.6 68.0 58.5 XCOPA vie HR Acc. 80.6 77.6 79.8 72.6 XWN zho HR Acc. 68.5 65.0 64.7 56.5 XCOPA zho HR Acc. 80.6 81.6 83.6 76.8
TydiQA ara HR f1 76.9 76.8 77.1 78.5 XLSum mar LR rougeL 19.6 17.5 18.1 19.1 TydiQA ben MR f1 88.0 85.8 83.4 82.6 XLSum mya LR rougeL 15.6 14.6 14.4 14.0 TydiQA eng HR f1 75.4 74.1 74.9 70.4 XLSum npi LR rougeL 25.7 24.5 24.6 23.8 TydiQA fin HR f1 76.0 76.2 76.8 74.3 XLSum orm LR rougeL 13.6 11.4 12.8 11.6 TydiQA ind MR f1 78.4 78.6 80.2 78.2 XLSum pan LR rougeL 27.8 26.4 26.4 25.8 TydiQA jpn HR f1 72.7 69.5 69.8 68.0 XLSum pbt LR rougeL 33.5 32.1 31.8 30.4 TydiQA kor HR f1 76.5 75.0 76.2 72.8 XLSum pes HR rougeL 29.8 28.1 28.3 28.2 TydiQA rus HR f1 75.4 74.6 75.4 76.1 XLSum pidgin LR rougeL 22.8 20.4 21.1 22.7 TydiQA swa LR f1 83.4 82.6 83.3 78.9 XLSum por HR rougeL 29.9 29.0 28.8 28.3 TydiQA tel LR f1 87.6 86.5 85.6 84.4 XLSum run LR rougeL 24.9 24.3 24.0 23.0 TydiQA tha MR f1 75.9 75.6 74.6 73.6 XLSum rus HR rougeL 27.7 26.7 26.8 25.8 XLSum amh LR rougeL 19.9 18.8 19.1 18.2 XLSum sin LR rougeL 20.8 20.0 20.0 19.6 XLSum ara HR rougeL 28.4 27.2 26.2 27.9 XLSum som LR rougeL 25.4 24.6 24.6 24.2 XLSum azj LR rougeL 20.7 20.2 19.9 18.5 XLSum spa HR rougeL 24.2 22.1 22.8 22.5 XLSum ben MR rougeL 27.7 26.3 26.5 25.7 XLSum srp HR rougeL 19.3 18.2 18.5 17.8 XLSum cym LR rougeL 26.7 26.1 26.4 25.3 XLSum swa LR rougeL 32.3 30.3 30.3 30.1 XLSum eng HR rougeL 30.6 29.2 29.3 28.6 XLSum tam MR rougeL 19.8 18.5 18.8 18.1 XLSum fra HR rougeL 28.6 28.3 28.3 28.2 XLSum tel LR rougeL 18.0 16.9 17.4 15.2 XLSum gla LR rougeL 27.6 26.3 26.9 24.3 XLSum tha MR rougeL 12.0 10.5 10.8 10.1 XLSum guj LR rougeL 22.3 20.5 20.8 20.7 XLSum tir LR rougeL 19.4 16.2 18.6 17.9 XLSum hau LR rougeL 32.2 31.5 31.6 30.7 XLSum tur HR rougeL 28.7 27.4 27.3 27.2 XLSum hin HR rougeL 33.8 32.8 32.8 32.3 XLSum ukr MR rougeL 22.5 21.8 21.8 20.7 XLSum ibo LR rougeL 26.1 24.4 25.1 20.4 XLSum urd MR rougeL 33.7 32.5 32.8 32.0 XLSum ind MR rougeL 31.6 30.0 30.5 30.1 XLSum uzb MR rougeL 16.3 16.1 15.9 15.8 XLSum jpn HR rougeL 7.9 6.7 7.0 7.2 XLSum vie HR rougeL 27.5 26.5 26.3 25.4 XLSum kir LR rougeL 17.3 16.6 16.5 16.2 XLSum yor LR rougeL 25.1 23.5 24.2 22.2 XLSum kor HR rougeL 18.2 16.4 16.5 16.2 XLSum zho HR rougeL 5.4 4.4 4.3 5.4
to English (X→eng) from English (eng→X) FLORES ace LR spBleu 7.8 7.9 6.3 6.2 FLORES ace LR spBleu 0.9 1.3 1.0 2.2 chrF++ 32.8 32.3 31.9 27.9 chrF++ 11.9 13.6 12.9 19.6 FLORES acm LR spBleu 22.6 27.3 22.6 18.9 FLORES acm LR spBleu 15.7 15.2 14.6 12.5 chrF++ 52.4 54.1 53.7 44.9 chrF++ 38.5 39.1 38.7 34.7 FLORES acq LR spBleu 23.7 29.5 25.5 20.0 FLORES acq LR spBleu 17.1 15.5 15.8 13.8 chrF++ 53.2 55.4 55.6 45.8 chrF++ 39.3 39.5 39.5 35.4 FLORES aeb LR spBleu 18.8 22.6 17.6 17.0 FLORES aeb LR spBleu 14.2 13.3 13.1 11.3 chrF++ 49.1 50.8 49.9 42.8 chrF++ 35.7 36.0 35.9 32.5 FLORES afr MR spBleu 41.9 48.3 47.1 31.1 FLORES afr MR spBleu 35.7 39.3 39.8 27.8 chrF++ 64.3 68.3 68.2 55.2 chrF++ 58.4 61.6 61.7 51.8 FLORES ajp LR spBleu 28.3 32.6 28.7 20.6 FLORES ajp LR spBleu 15.4 15.4 15.3 11.9 chrF++ 55.4 57.3 57.3 45.8 chrF++ 38.9 40.0 39.9 34.7 FLORES amh LR spBleu 20.8 25.5 20.4 19.2 FLORES amh LR spBleu 11.6 8.6 8.4 11.9 chrF++ 49.8 51.9 51.0 44.6 chrF++ 26.6 25.8 25.5 23.9 FLORES apc LR spBleu 24.3 30.2 25.5 19.1 FLORES apc LR spBleu 15.0 15.2 15.4 12.0
chrF++ 52.8 55.4 55.1 44.4 chrF++ 38.1 39.0 39.1 34.4
FLORES arb LR spBleu 26.4 32.1 26.8 20.9 FLORES arb LR spBleu 20.9 20.8 21.9 16.0 chrF++ 54.7 57.1 57.1 46.6 chrF++ 41.7 43.2 43.6 37.4 FLORES ars LR spBleu 25.6 32.0 26.4 20.6 FLORES ars LR spBleu 18.7 19.9 18.5 15.6 chrF++ 54.3 56.8 56.6 46.2 chrF++ 40.9 42.7 42.1 36.9 FLORES ary LR spBleu 16.9 20.5 14.4 15.1 FLORES ary LR spBleu 10.9 11.1 10.4 9.0 chrF++ 47.0 48.3 46.6 40.5 chrF++ 32.6 33.4 33.0 30.1 FLORES arz LR spBleu 22.6 27.5 21.6 18.2 FLORES arz LR spBleu 14.4 13.8 14.6 11.4 chrF++ 51.6 53.4 52.4 43.8 chrF++ 35.7 36.2 36.4 32.7 FLORES azb LR spBleu 9.5 9.8 8.3 7.8 FLORES azb LR spBleu 0.1 0.1 0.1 0.1 chrF++ 39.6 39.2 38.7 33.9 chrF++ 0.6 0.6 0.6 0.5 FLORES azj LR spBleu 20.4 23.2 19.0 17.8 FLORES azj LR spBleu 17.0 17.0 17.8 12.4 chrF++ 49.0 50.2 49.6 43.4 chrF++ 40.4 41.3 41.3 35.8 FLORES bel MR spBleu 17.8 23.7 17.5 17.6 FLORES bel MR spBleu 18.2 19.4 19.9 14.0 chrF++ 48.9 51.1 50.1 43.8 chrF++ 36.6 38.0 38.5 32.6 FLORES ben MR spBleu 23.6 29.0 24.0 20.4 FLORES ben MR spBleu 17.2 16.7 18.2 15.0 chrF++ 52.3 54.2 53.7 45.5 chrF++ 39.3 40.7 41.6 36.6 FLORES bjn LR spBleu 11.4 13.4 10.1 8.7 FLORES bjn LR spBleu 1.8 2.4 1.6 2.9 chrF++ 36.7 36.9 36.6 30.6 chrF++ 20.1 22.0 19.3 21.6 FLORES bul MR spBleu 30.3 37.1 34.6 23.9 FLORES bul MR spBleu 33.1 36.3 36.3 22.2 chrF++ 57.4 60.6 60.8 49.4 chrF++ 53.7 56.6 57.1 44.8 FLORES cat HR spBleu 37.8 41.8 41.5 27.4 FLORES cat HR spBleu 34.7 37.3 37.7 26.9 chrF++ 61.2 63.8 64.4 52.2 chrF++ 56.7 59.1 59.4 49.8 FLORES ceb MR spBleu 35.7 40.2 33.9 27.4 FLORES ceb MR spBleu 24.9 25.0 25.5 19.6 chrF++ 59.3 61.4 61.1 51.0 chrF++ 52.7 53.4 54.0 47.2 FLORES ces HR spBleu 32.1 35.8 33.6 24.1 FLORES ces HR spBleu 25.4 27.4 29.4 17.9 chrF++ 57.0 59.4 59.7 49.6 chrF++ 45.9 48.1 49.5 38.7 FLORES ckb LR spBleu 16.7 20.7 15.9 14.6 FLORES ckb LR spBleu 0.2 0.2 0.2 1.2 chrF++ 46.9 48.8 47.7 40.3 chrF++ 0.5 0.5 0.4 19.6 FLORES cym LR spBleu 37.4 44.7 42.4 28.3 FLORES cym LR spBleu 29.5 30.9 29.6 22.8 chrF++ 61.6 65.2 65.5 52.3 chrF++ 50.5 51.5 50.7 44.4 FLORES dan MR spBleu 39.0 43.7 43.3 29.1 FLORES dan MR spBleu 32.4 37.6 36.8 24.1 chrF++ 62.1 65.1 65.4 53.4 chrF++ 55.9 59.8 60.1 48.2 FLORES deu HR spBleu 37.0 39.8 38.1 26.8 FLORES deu HR spBleu 9.9 28.5 13.9 8.3 chrF++ 60.0 62.2 62.2 51.5 chrF++ 46.0 54.6 52.0 42.3 FLORES ell MR spBleu 29.6 33.5 28.6 22.3 FLORES ell MR spBleu 26.5 28.9 29.0 21.1 chrF++ 55.0 57.4 57.0 47.5 chrF++ 44.8 47.2 47.3 40.1 FLORES epo LR spBleu 36.6 40.3 40.1 27.5 FLORES epo LR spBleu 33.4 36.3 36.5 24.8 chrF++ 59.5 62.4 62.9 51.8 chrF++ 56.9 59.1 59.5 49.5 FLORES est MR spBleu 27.7 34.5 29.4 22.4 FLORES est MR spBleu 23.0 23.5 24.9 17.5 chrF++ 55.3 58.0 57.9 47.7 chrF++ 48.7 50.7 51.1 42.7 FLORES eus HR spBleu 25.9 30.4 23.4 21.1 FLORES eus HR spBleu 18.6 15.8 16.0 14.0 chrF++ 52.6 54.6 53.7 46.3 chrF++ 47.0 45.5 46.0 41.5 FLORES fin HR spBleu 26.8 32.1 28.1 22.1 FLORES fin HR spBleu 21.9 22.1 23.5 15.2 chrF++ 54.2 56.5 56.2 47.5 chrF++ 48.0 49.6 50.3 41.8 FLORES fra HR spBleu 36.9 41.4 39.8 27.7 FLORES fra HR spBleu 36.7 41.8 40.0 29.9 chrF++ 60.4 63.0 63.1 52.3 chrF++ 58.8 61.5 61.7 51.8 FLORES gla LR spBleu 25.3 28.1 23.8 20.4 FLORES gla LR spBleu 16.8 15.9 15.0 12.5 chrF++ 52.1 53.6 53.2 44.7 chrF++ 42.6 43.1 42.2 38.5 FLORES gle LR spBleu 32.3 37.0 32.4 23.7 FLORES gle LR spBleu 20.6 20.9 21.4 14.5 chrF++ 56.8 59.4 58.7 48.2 chrF++ 44.2 45.0 45.1 38.9 FLORES glg MR spBleu 36.8 39.7 37.3 26.4 FLORES glg MR spBleu 30.9 33.0 34.2 24.1 chrF++ 60.2 62.5 62.5 51.3 chrF++ 54.8 56.4 57.5 48.7 FLORES guj LR spBleu 26.8 32.2 27.8 21.7 FLORES guj LR spBleu 20.1 19.0 17.0 15.0 chrF++ 54.8 57.1 56.8 47.5 chrF++ 41.7 42.3 39.6 36.1 FLORES hat LR spBleu 29.8 35.1 30.7 23.7 FLORES hat LR spBleu 22.6 23.3 22.4 19.4 chrF++ 56.2 58.3 58.1 48.5 chrF++ 47.2 48.8 48.8 42.6 FLORES hau LR spBleu 22.6 26.1 19.0 19.3 FLORES hau LR spBleu 11.6 10.8 8.4 11.0 chrF++ 49.0 50.3 49.3 42.7 chrF++ 41.8 41.9 40.8 38.4 FLORES heb LR spBleu 32.1 36.0 29.2 23.4 FLORES heb LR spBleu 19.2 19.1 19.6 13.8 chrF++ 57.4 59.5 58.8 48.9 chrF++ 41.6 43.0 43.5 35.4 FLORES hin HR spBleu 29.6 34.3 29.6 23.1 FLORES hin HR spBleu 22.7 22.8 22.2 17.9 chrF++ 55.4 57.8 57.5 48.3 chrF++ 44.1 44.9 44.5 38.9 FLORES hun HR spBleu 27.8 32.8 28.0 22.6 FLORES hun HR spBleu 24.0 23.7 24.7 17.6 chrF++ 54.5 57.0 56.6 47.9 chrF++ 47.1 47.9 48.5 41.0 FLORES hye LR spBleu 28.1 33.2 27.5 22.5 FLORES hye LR spBleu 26.1 27.3 28.0 20.1 chrF++ 55.3 57.6 57.4 47.9 chrF++ 47.1 48.2 49.0 41.6 FLORES ibo LR spBleu 16.4 17.8 13.1 16.7 FLORES ibo LR spBleu 9.6 8.6 8.3 10.4 chrF++ 45.0 45.3 43.9 40.3 chrF++ 32.8 33.3 33.1 32.3 FLORES ind MR spBleu 34.5 38.6 35.6 26.4 FLORES ind MR spBleu 27.1 19.5 22.4 23.2 chrF++ 59.0 61.5 61.5 51.2 chrF++ 56.5 56.0 57.7 51.3
FLORES isl LR spBleu 25.8 32.9 27.1 21.8 FLORES isl LR spBleu 20.6 22.0 22.2 15.1 chrF++ 52.8 55.6 54.9 46.2 chrF++ 41.5 42.9 43.4 35.8 FLORES ita HR spBleu 32.6 35.1 32.3 24.9 FLORES ita HR spBleu 27.0 28.7 28.4 20.2 chrF++ 56.8 58.8 58.6 49.7 chrF++ 51.4 53.0 52.9 45.2 FLORES jav LR spBleu 27.5 34.2 27.6 23.7 FLORES jav LR spBleu 19.6 16.5 12.8 14.5 chrF++ 55.2 57.6 56.7 47.7 chrF++ 48.4 48.3 46.9 43.0 FLORES jpn HR spBleu 20.2 21.9 17.6 17.3 FLORES jpn HR spBleu 18.2 14.7 18.2 11.3 chrF++ 48.5 49.4 48.8 43.3 chrF++ 29.7 29.9 31.8 23.7 FLORES kan LR spBleu 22.3 27.6 22.1 19.6 FLORES kan LR spBleu 20.8 19.8 19.6 14.3 chrF++ 51.3 53.6 52.6 45.5 chrF++ 43.7 44.9 44.6 36.9 FLORES kas LR spBleu 8.2 9.8 7.4 5.9 FLORES kas LR spBleu 0.4 0.2 0.2 0.1 chrF++ 38.3 39.4 37.7 31.8 chrF++ 10.1 8.6 8.7 8.6 FLORES kat MR spBleu 21.9 27.4 22.8 19.3 FLORES kat MR spBleu 20.8 19.7 21.4 14.5 chrF++ 51.3 53.3 52.9 45.5 chrF++ 42.3 42.9 43.7 36.7 FLORES kau LR spBleu 1.7 1.4 1.4 2.0 FLORES kau LR spBleu 0.6 0.5 0.5 0.9 chrF++ 18.0 16.5 16.9 18.6 chrF++ 9.6 8.4 9.1 11.9 FLORES kaz MR spBleu 23.9 30.0 23.8 20.0 FLORES kaz MR spBleu 20.8 21.0 21.1 14.1 chrF++ 51.6 54.3 53.6 45.4 chrF++ 45.7 47.4 47.2 39.7 FLORES khk LR spBleu 19.3 22.5 17.2 17.5 FLORES khk LR spBleu 17.8 16.0 16.2 14.1 chrF++ 48.4 50.0 49.3 43.1 chrF++ 41.1 40.6 41.3 36.5 FLORES khm LR spBleu 23.1 28.1 22.3 21.5 FLORES khm LR spBleu 15.1 12.1 12.4 11.1 chrF++ 52.0 54.2 53.4 46.5 chrF++ 38.6 38.1 38.6 33.7 FLORES kir LR spBleu 18.6 23.2 18.3 16.1 FLORES kir LR spBleu 14.2 10.8 10.6 10.2 chrF++ 47.2 48.9 48.3 41.5 chrF++ 38.1 38.0 37.5 33.8 FLORES kor HR spBleu 20.4 25.3 21.1 18.3 FLORES kor HR spBleu 13.6 13.7 14.8 11.3 chrF++ 49.9 51.4 51.2 43.8 chrF++ 24.4 25.7 26.0 20.7 FLORES kur LR spBleu 18.6 23.6 17.7 18.0 FLORES kur LR spBleu 9.7 9.9 7.4 0.2 chrF++ 48.1 49.9 49.1 41.8 chrF++ 33.4 34.4 32.0 0.6 FLORES lao LR spBleu 25.7 30.4 24.7 22.2 FLORES lao LR spBleu 25.3 23.7 27.1 16.2 chrF++ 53.7 55.9 55.4 46.7 chrF++ 44.7 45.6 47.1 37.0 FLORES lav LR spBleu 26.9 33.5 28.2 22.3 FLORES lav LR spBleu 23.6 23.4 25.0 18.6 chrF++ 54.9 57.6 57.4 48.0 chrF++ 48.2 49.3 50.5 43.1 FLORES lit MR spBleu 26.3 31.1 25.4 20.5 FLORES lit MR spBleu 22.5 22.2 22.6 17.9 chrF++ 53.1 55.1 54.8 45.9 chrF++ 47.2 48.4 48.9 42.1 FLORES ltz LR spBleu 36.2 40.7 37.9 26.6 FLORES ltz LR spBleu 13.5 21.1 16.0 16.0 chrF++ 60.2 62.8 62.7 51.0 chrF++ 45.6 48.1 47.0 41.9 FLORES mal LR spBleu 25.0 29.3 24.9 20.8 FLORES mal LR spBleu 21.4 18.7 19.0 15.8 chrF++ 53.0 54.9 54.6 46.4 chrF++ 43.9 44.1 44.7 37.9 FLORES mar LR spBleu 24.0 27.1 23.4 20.4 FLORES mar LR spBleu 14.1 11.9 11.8 9.1 chrF++ 52.4 54.4 53.8 46.1 chrF++ 39.6 38.9 38.7 33.3 FLORES mkd LR spBleu 33.0 37.8 34.4 25.0 FLORES mkd LR spBleu 29.6 32.7 33.0 21.8 chrF++ 58.3 61.0 61.2 50.4 chrF++ 52.5 55.5 55.7 45.2 FLORES mlt LR spBleu 39.5 43.8 40.1 29.5 FLORES mlt LR spBleu 27.6 28.6 28.1 23.6 chrF++ 62.5 65.4 65.5 53.6 chrF++ 49.9 51.8 51.8 46.3 FLORES mni LR spBleu 3.6 3.4 3.3 2.1 FLORES mni LR spBleu 0.7 0.3 1.0 0.9 chrF++ 27.2 25.7 26.4 22.5 chrF++ 5.2 1.0 11.3 12.6 FLORES mri LR spBleu 16.3 19.5 14.5 17.4 FLORES mri LR spBleu 20.4 19.2 19.7 17.4 chrF++ 44.8 46.2 45.2 40.4 chrF++ 43.8 43.6 43.8 40.2 FLORES msa LR spBleu 17.7 21.1 16.2 13.6 FLORES msa LR spBleu 2.8 2.5 2.1 2.8 chrF++ 47.1 49.3 47.7 38.8 chrF++ 28.8 28.2 25.9 21.1 FLORES mya LR spBleu 17.0 19.4 15.5 17.3 FLORES mya LR spBleu 14.6 13.0 12.6 11.8 chrF++ 47.0 48.1 47.6 42.6 chrF++ 42.8 42.6 42.8 39.0 FLORES nld HR spBleu 29.8 33.0 30.5 23.2 FLORES nld HR spBleu 25.3 28.6 28.4 18.1 chrF++ 54.5 56.9 56.4 48.5 chrF++ 49.8 52.8 52.8 43.5 FLORES nno LR spBleu 35.8 41.0 39.1 27.3 FLORES nno LR spBleu 25.1 23.7 25.8 18.7 chrF++ 59.8 62.7 62.7 51.5 chrF++ 49.5 50.8 52.0 43.1 FLORES nob LR spBleu 35.3 39.9 38.9 26.5 FLORES nob LR spBleu 25.2 29.6 30.4 18.7 chrF++ 59.1 62.2 62.1 51.0 chrF++ 49.8 53.7 54.5 43.2 FLORES npi LR spBleu 26.9 31.6 27.4 22.0 FLORES npi LR spBleu 20.1 19.3 20.2 12.9 chrF++ 54.8 57.3 57.0 47.6 chrF++ 45.0 45.8 46.8 38.1 FLORES nso LR spBleu 21.7 23.1 17.4 17.3 FLORES nso LR spBleu 6.0 5.9 5.4 6.1 chrF++ 48.9 49.5 48.7 40.5 chrF++ 30.1 30.5 29.9 29.5 FLORES pbt LR spBleu 20.2 26.0 20.9 18.8 FLORES pbt LR spBleu 8.7 7.3 7.1 4.9 chrF++ 50.0 52.3 51.5 44.0 chrF++ 29.0 28.2 27.4 24.6 FLORES pes LR spBleu 26.1 30.7 25.1 21.3 FLORES pes LR spBleu 22.8 23.8 23.3 16.8 chrF++ 53.7 56.2 55.7 46.8 chrF++ 42.8 44.0 44.1 37.7 FLORES plt LR spBleu 21.8 27.5 21.4 19.8 FLORES plt LR spBleu 21.4 21.5 20.6 15.8 chrF++ 49.5 51.4 50.6 43.7 chrF++ 49.1 50.0 49.5 44.1 FLORES pol HR spBleu 26.6 30.1 28.1 21.1 FLORES pol HR spBleu 21.7 22.7 24.5 16.2 chrF++ 52.8 54.5 54.7 46.0 chrF++ 42.9 44.4 45.4 37.2

heading: J Model Card
text: Model Card for the Aya Model
The Aya model is a massively multilingual LLM, open-source model, instruction-finetuned on 101 languages. It vastly improves over all other massively multilingual open-source models, on a range of automatic and human evaluations.
• Curated by: Cohere For AI
• Language(s): 101 languages
• License: Apache 2.0
• Repository: https://hf.co/CohereForAI/aya-101
Authorship
Publishing Organization: Cohere For AI
Industry Type: Not-for-profit - Tech
Contact Details: https://aya.for.ai/
Training
Training Data
• xP3x
• Aya Collection
• Aya Dataset
• Data provenance collection
• Translated Synthetic generations
Training Factors
• Pretraining model: mT5
• Model sizes: 13B parameters
• Training Budget: 25M samples
• Training Languages: 101
• Infra: TPU v4, T5x library
Evaluation
A new set of comprehensive multilingual evaluations are introduced which include 99 languages and 8 types of tasks. They cover unseen discriminative tasks (XWinograd, XNLI, XCOPA, XStoryCloze), Multilingual MMLU, generative tasks (FLORES-200, XLSum, Tydi-QA) along with human and LLM preference evals using the Aya Evaluation Suite.
Bias, Risks, and Limitation
For a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer Sections 6 and 7 of this paper. We hope that the release of the Aya model will make communitybased redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.
Model Version and Maintenance
Maintenance Status Actively Maintained Model Dates: Dec 2023 - Feb 2024
Version Details Current version: 1.0 First Release: 02/2024

heading: Maintenance Plan
text: No updates planned.

heading: Future Work
text: , 2023b), we leave it for future work