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August 8, 2025A new philosophy for developing LLM architectures reduces energy requirements, speeds up runtime, and preserves pretrained-model performance.
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PRX Quantum2024We take a bottom-up first-principles approach to designing a two-qubit gate between fluxonium qubits for minimal error, speed, and control simplicity. Our proposed architecture consists of two fluxoniums coupled via a resonator. The use of a simple linear coupler has many practical benefits, including the possibility of material optimization for suppressing loss, reducing fabrication complexity, and increasing
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ACL 2024 Workshop on Fact Extraction and Verification2024The ability to extract and verify factual information from free-form text is critical in an era where vast amounts of unstructured data are available, yet unreliable sources abound. This paper focuses on enhancing causal deductive reasoning, a key component of factual verification, through the lens of accident investigation, where determining the probable causes of events is paramount. Deductive reasoning
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2024Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instructions and examples. However, LLMs can fail to generate predictions that satisfy group fairness, that is, produce equitable outcomes across groups. Critically, conventional debiasing approaches for natural language tasks do not
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2024Multilingual ASR offers training, deployment and overall performance benefits, but models trained via simple data pooling are known to suffer from cross-lingual interference. Oracle language information (exact-prior) and language-specific parameters are usually leveraged to overcome this, but such approaches cannot enable seamless, truly multilingual experiences. Existing methods try to overcome this limitation
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Prompt engineering has emerged as a powerful technique for optimizing large language models (LLMs) for specific applications, enabling faster prototyping and improved performance, and giving rise to the interest of the community in protecting proprietary system prompts. In this work, we explore a novel perspective on prompt privacy through the lens of membership inference. We develop Prompt Detective, a
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