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Topic knowledge based controlled generation for long documents using retrieval-based language modelsFSDM 20232023Current LLM summarization systems Produce broad overviews which are disconnected from people specific interests and expectations. Basically, people preferences (topics) can be expressed by a collection of semantic keywords. Previous work exploit these keywords as extra input to generate summary. That requires additional human annotations. To tackle these constraints, we propose a novel framework, Topic
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CIKM 2023 Workshop Personalized Generative AI2023Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language process- ing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model’s output, a straightforward approach is to incorporate past user data into
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NeurIPS 2023 Workshop on SyntheticData4ML2023We present CALICO, a method to fine-tune Large Language Models (LLMs) to localize conversational agent training data from one language to another. For slots (named entities), CALICO supports three operations: verbatim copy, literal translation, and localization, i.e. generating slot values more appropriate in the target language, such as city and airport names located in countries where the language is
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NeurIPS 2023 Workshop on SyntheticData4ML2023The emergence of Large Language Models (LLMs) with capabilities like In-Context Learning (ICL) has ushered in new possibilities for data generation across various domains while minimizing the need for extensive data collection and modeling techniques. Researchers have explored ways to use this generated synthetic data to optimize smaller student models for reduced deployment costs and lower latency in downstream
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EMNLP 20232023A particularly successful class of approaches for few-shot learning combines language models with prompts — handcrafted task descriptions that complement data samples. However, designing prompts by hand for each task commonly requires domain knowledge and substantial guesswork. We observe, in the context of classification tasks, that instruction-finetuned language models are remarkably robust towards some
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Projection image adapted from Michael Horvath under the CC BY-SA 4.0 licenseJanuary 15, 2019Neural networks have been responsible for most of the top-performing AI systems of the past decade, but they tend to be big, which means they tend to be slow. That’s a problem for systems like Alexa, which depend on neural networks to process spoken requests in real time. -
December 21, 2018In May 2018, Amazon launched Alexa’s Remember This feature, which enables customers to store “memories” (“Alexa, remember that I took Ben’s watch to the repair store”) and recall them later by asking open-ended questions (“Alexa, where is Ben’s watch?”).
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December 18, 2018At a recent press event on Alexa's latest features, Alexa’s head scientist, Rohit Prasad, mentioned multistep requests in one shot, a capability that allows you to ask Alexa to do multiple things at once. For example, you might say, “Alexa, add bananas, peanut butter, and paper towels to my shopping list.” Alexa should intelligently figure out that “peanut butter” and “paper towels” name two items, not four, and that bananas are a separate item.
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December 17, 2018In recent years, data representation has emerged as an important research topic within machine learning.
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December 13, 2018Language models are a key component of automatic speech recognition systems, which convert speech into text. A language model captures the statistical likelihood of any particular string of words, so it can help decide between different interpretations of the same sequence of sounds.
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December 11, 2018Suppose that you say to Alexa, “Alexa, play Mary Poppins.” Alexa must decide whether you mean the book, the video, or the soundtrack. How should she do it?
