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

A case of a bleeding benign gastrocolic fistula in 2014

INTRODUCTION: In the era of proton pump inhibitors in the treatment of peptic ulcer disease, the incidence of a gastrocolic fistula arising from unoperated gastric ulcers is extremely low. PRESENTATION OF CASE: We present the case of a 68-year old farmer who presented with melaena and was found to have a benign gastrocolic fistula in the setting of untreated peptic ulcer disease, chronic NSAID ingestion and heavy alcohol intake. The diagnosis was made by gastroscopy. En bloc surgery was undertaken due to the size of the fistula and concomitant significant bleeding of the ulcer which would not have made it amenable to medical management. DISCUSSION: The symptoms of a gastrocolic fistula are undifferentiated and the diagnosis can easily be missed in the setting of other complications such as bleeding or perforation of a hollow viscus. Barium enamas are the most accurate for the diagnosis but gastroscopy with biopsy is usually performed to rule out malignancy. The mainstay of treatment is usually surgical, though patients can be medically managed if he/she is not a surgical candidate. CONCLUSION: Benign gastrocolic fistulas are rare and its diagnosis is easily missed

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