Language models carry implicit distributional biases based on their training data, which can reinforce existing norms. In this work, we take one step towards addressing the challenge of unwanted biases by enabling language models to return outputs with a broader spectrum of attribute traits, specified by a user. This is achieved by asking language models to evaluate and modify their outputs.
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The research compares Luminous to the models from GPT-3 and ChatGPT developer OpenAI, among others. The scientific comparison included tasks related to text classification, evaluation, and generation, as well as answering questions about specific text contents. The result is impressive – with Luminous, a European AI language model is, for the first time, on par with the world's leading AI language models, while being much more efficient.
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Aleph Alpha and our partner Graphcore are unveiling a significant advance in AI compute efficiency, with the sparsification of a 13bn parameter model down to just 2.6bn parameters.
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In this post we present our semantic embedding model Luminous-Explore, show state-of-the-art results on two public benchmarks and go into detail on the philosophy and practical use of semantic representations.
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