![]() We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. ![]() An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. ![]() The teams goal is to optimize and automate business decision making in the EU business and beyond. EDS is an economic science team based in the EU Stores business. We hope our work presents a compelling case for seq2seq models as a powerful alternative to decoder-only models for LLM training.Īre you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. We reiterate the importance of task-specific fairness auditing and emphasize the need for more research on bias measurement and mitigation from the community.Īll in all, we demonstrated in our work that the proposed style of pretraining enables seq2seq models that outperform much larger decoder-only LLMs across different tasks, both in a few-shot setting and with fine-tuning. Depending on the downstream application that AlexaTM 20B is being applied to, one or several of the prior techniques from the literature might be used to detoxify and debias the model. Therefore, we recommend that users conduct a full task-specific fairness-and-bias analysis before using the model to fully understand and address any potential harm that might arise from its use. In an analysis reported in our paper, we found that AlexaTM 20B, like other LLMs, has some likelihood of reproducing toxic language, social biases, and harmful stereotypes found in its training data. This provides a more flexible way for researchers to use AlexaTM 20B in their own work. We have also implemented a function to enable loading the model on up to eight GPUs with limited GPU memory for running inference on instances of Amazon Web Services’ EC2 computation service. We will be releasing the model publicly for non-commercial use to aid the development and evaluation of multilingual large language models (LLMs). The input to the encoder is in the yellow box, the decoder’s output in the pink box.ĪlexaTM 20B is the largest multilingual seq2seq model to date that is also capable of few-shot learning. News summarization by AlexaTM 20B when given only a single example. This allows us to develop new features more rapidly, and in multiple languages, simultaneously. ![]() The model can generalize from these to the unfamiliar intent get-news-update and generate utterances corresponding to that intent in different languages. In the example below, the model is provided with three examples of different intents, or tasks that the customer wants executed: book-restaurant, play-music, and get-weather. The experiments reported in the paper - which use only publicly available data - show that AlexaTM 20B can not only transfer what it learns across languages but also learn new tasks from just a handful of examples (few-shot learning). In a follow-up paper, which we've published on arXiv, we have taken this line of research a step further, with a 20-billion-parameter generative model called AlexaTM 20B. In a paper we’re presenting at this year’s Knowledge Discovery and Data Mining Conference (KDD), we showed that 10-billion- and two-billion-parameter AlexaTM models can improve on state-of-art cross-lingual transfer learning and increase Alexa’s accuracy in different locales. New method would enable BERT-based natural-language-processing models to handle longer text strings, run in resource-constrained settings - or sometimes both. ![]()
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