BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Multilingual AUT Repository for Automatic Verbal Creativity Assessment

In an international and collaborative effort, we have collected 28 AUT datasets across 11 languages. Each dataset varies on a number of parameters including participants, instructions, number of raters, and additional measures that may be used for secondary analyses (e.g., creative achievements). Please find additional details of our first analysis of these data in a forthcoming paper titled "Multilingual Semantic Distance: Automatic Verbal Creativity Assessment in Many Languages"

Multilingual semantic distance: Automatic verbal creativity assessment in many languages

Creativity research commonly involves recruiting human raters to judge the originality of responses to divergent thinking tasks, such as the alternate uses task (AUT). These manual scoring practices have benefited the field, but they also have limitations, including labor-intensiveness and subjectivity, which can adversely impact the reliability and validity of assessments. To address these challenges, researchers are increasingly employing automatic scoring approaches, such as distributional models of semantic distance. However, semantic distance has primarily been studied in English-speaking samples, with very little research in the many other languages of the world. In a multilab study (N = 6,522 participants), we aimed to validate semantic distance on the AUT in 12 languages: Arabic, Chinese, Dutch, English, Farsi, French, German, Hebrew, Italian, Polish, Russian, and Spanish. We gathered AUT responses and human creativity ratings (N = 107,672 responses), as well as criterion measures for validation (e.g., creative achievement). We compared two deep learning-based semantic models—multilingual bidirectional encoder representations from transformers and cross-lingual language model RoBERTa—to compute semantic distance and validate this automated metric with human ratings and criterion measures. We found that the top-performing model for each language correlated positively with human creativity ratings, with correlations ranging from medium to large across languages. Regarding criterion validity, semantic distance showed small-to-moderate effect sizes (comparable to human ratings) for openness, creative behavior/achievement, and creative self-concept. We provide open access to our multilingual dataset for future algorithmic development, along with Python code to compute semantic distance in 12 languages

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License