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The introduction of AutoData, an agent that searches directly over executable selection algorithms, suggests that data engineering can be treated as an agentic machine learning problem, extending autonomous research from model and training-code optimization to the data.
This paper introduces a simple two-hop question answering setting, where answering a question requires making inferences over two multilingual documents, and finds that language models are more sensitive to language variation in answer-span documents than in those providing bridging information, despite the equal impor...
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