In order for large language models to be useful across the globe, they are fine-tuned to follow instructions on multilingual data. Despite the ubiquity of such post-training, a clear understanding of the dynamics that enable cross-lingual transfer remains elusive. This study examines cross-lingual transfer (CLT) dynamics in realistic post-training settings. We study two model families of up to 35B parameters in size trained on carefully controlled mixtures of multilingual data on three generative tasks with varying levels of complexity (summarization, instruction following, and mathematical reasoning) in both single-task and multi-task instruction tuning settings. Overall, we find that the dynamics of cross-lingual transfer and multilingual performance cannot be explained by isolated variables, varying depending on the combination of post-training settings. Finally, we identify the conditions that lead to effective cross-lingual transfer in practice.

Retrieval systems generally focus on web-style queries that are short and underspecified. However, advances in language models have facilitated the nascent rise of retrieval models that can understand more complex queries with diverse intents. However, these efforts have focused exclusively on English; therefore, we do not yet understand how they work across languages. We introduce mFollowIR, a multilingual benchmark for measuring instruction-following ability in retrieval models. mFollowIR builds upon the TREC NeuCLIR narratives (or instructions) that span three diverse languages (Russian, Chinese, Persian) giving both query and instruction to the retrieval models. We make small changes to the narratives and isolate how well retrieval models can follow these nuanced changes. We present results for both multilingual (XX-XX) and cross-lingual (En-XX) performance. We see strong cross-lingual performance with English-based retrievers that trained using instructions, but find a notable drop in performance in the multilingual setting, indicating that more work is needed in developing data for instruction-based multilingual retrievers.

Multilingual language models often perform unevenly across different languages due to limited generalization capabilities for some languages. This issue is significant because of the growing interest in making universal language models that work well for all languages. Instruction tuning with multilingual instruction-response pairs has been used to improve model performance across various languages. However, this approach is challenged by high computational costs, a lack of quality tuning data for all languages, and the "curse of multilinguality" -- the performance drop per language after adding many languages. Recent studies have found that working with datasets with few languages and a smaller number of instances can be beneficial. Yet, there exists no systematic investigation into how choosing different languages affects multilingual instruction tuning. Our study proposes a method to select languages for instruction tuning in a linguistically informed way, aiming to boost model performance across languages and tasks. We use a simple algorithm to choose diverse languages and test their effectiveness on various benchmarks and open-ended questions. Our results show that this careful selection generally leads to better outcomes than choosing languages at random. We suggest a new and simple way of enhancing multilingual models by selecting diverse languages based on linguistic features that could help develop better multilingual systems and guide dataset creation efforts. All resources, including the code for language selection and multilingual instruction tuning, are made available in our official repository at https://github.com/GGLAB-KU/ling-informed-mit enabling reproducibility and further research in this area.

Multilingual generative models obtain remarkable cross-lingual in-context learning capabilities through pre-training on large-scale corpora. However, they still exhibit a performance bias toward high-resource languages and learn isolated distributions of multilingual sentence representations, which may hinder knowledge transfer across languages. To bridge this gap, we propose a simple yet effective cross-lingual alignment framework exploiting pairs of translation sentences. It aligns the internal sentence representations across different languages via multilingual contrastive learning and aligns outputs by following cross-lingual instructions in the target language. Experimental results show that even with less than 0.1 {\textperthousand} of pre-training tokens, our alignment framework significantly boosts the cross-lingual abilities of generative language models and mitigates the performance gap. Further analyses reveal that it results in a better internal multilingual representation distribution of multilingual models.

Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. While such efforts are often carried out in a single language, we empirically analyze cost-efficient strategies for multilingual scenarios. Our study employs the Alpaca dataset and machine translations of it to form multilingual data, which is then used to tune LLMs through either low-rank adaptation or full-parameter training. Under a controlled computation budget, comparisons show that multilingual tuning is on par or better than tuning a model for each language. Furthermore, multilingual tuning with downsampled data can be as powerful and more robust. Our findings serve as a guide for expanding language support through instruction tuning.

The vast majority of today's large language models are English-centric, having been pretrained predominantly on English text. Yet, in order to meet user expectations, models need to be able to respond appropriately in multiple languages once deployed in downstream applications. Given limited exposure to other languages during pretraining, crosslingual transfer is important for achieving decent performance in non-English settings. In this work, we investigate just how much multilinguality is required during finetuning to elicit strong cross-lingual generalisation across Figure 1: Input/output (IO) language agreement for a range of tasks and target languages. We find English (en), German (de), Bulgarian (bg) and Icelandic that, compared to English-only finetuning, multilingual (is) when instruction tuning on monolingual English instruction tuning with as few as three (Mono) or on multilingual data (Multi-Guanaco).