Customer-obsessed science


Research areas
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February 06, 2025Novel training procedure and decoding mechanism enable model to outperform much larger foundation model prompted to perform the same task.
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December 24, 2024
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December 24, 2024
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Featured news
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NAACL 20252025The rise of LLMs has deflected a growing portion of human-computer interactions towards LLM-based chatbots. The remarkable abilities of these models allow users to interact using long, diverse natural language text covering a wide range of topics and styles. Phrasing these messages is a time and effort consuming task, calling for an autocomplete solution to assist users. We present ChaI-TeA: Chat Interaction
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2025The training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions, hindering optimization and specialization. These challenges can undermine model generalization across tasks, resulting in reduced downstream performance. Recent research suggests that fine-tuning LLMs on carefully selected,
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As the demand for online A/B testing continues to rises for tech companies, the opportunity cost of conducting these experiments becomes increasingly significant. Consequently, there is a rising need for an efficient continuous monitoring system capable of early terminating experiments when necessary. Existing literature and tools primarily focuses on early terminating experiments with evidently significant
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NAACL 20252025The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models’ ability to follow the instruction hierarchy. We bridge
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Tabular data is one of the most common data formats found in the web and used in domains like finance, banking, e-commerce and medical. Although deep neural networks (DNNs) have demonstrated outstanding performance on homogeneous data such as visual, audio, and textual data, tree ensemble methods such as Gradient Boosted Decision Trees (GBDTs) are often the go-to choice for supervised machine learning problems
Academia
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