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2,285 papers
No single model outperformed the others in all the test areas, so many different LLMs were chosen to provide support for breast cancer health information.
Early architectural decisions shape building energy demand and life cycle carbon, yet digital support remains fragmented across modelling, simulation and performance workflows. This systematic review examines how digital twin (DT) frameworks, tools and workflows are applied at the architectural design stage to support...
This document is a white paper that decodes the source code of our universe. Anyone can intuitively understand the validity of this theory by feeding this text into a Large Language Model (LLM) such as Gemini to discuss and debug it. For humanity's next OS update, I hereby release this 'Specification of the Universe' a...
It is suggested that while LLMs may offer support in structuring and initiating power analysis, they cannot substitute for expert judgment and underscores the importance of critically evaluating LLM performance in statistically demanding tasks.
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Two adjacent but methodologically separate substrates currently coexist inside Anthropic's published record. The first is a body of mechanistic-interpretability work, most prominently On the Biology of a Large Language Model, that produces attribution graphs over sparse activation features and explicitly cautions that...
Large language model outputs fail in ways that the standard evaluation categories, factual error, refusal, hallucination, sycophancy, do not adequately capture. A separate class of failure appears at the level of output form: text that remains fluent, structured, and superficially credible while abandoning the inferent...
This report explores the iterative blind use of LLMs as bug-fixers, and unveils the existence of a steering vector which controls the editing propensity, suggesting that LLMs have an internal representation of ``buggy code", and that this representation is what is falsely activated to induce pseudo-bug fixing.
Large Language Models (LLMs) have achieved remarkable progress across natural language processing (NLP) tasks, yet their capabilities degrade sharply for low-resource languages and dialectally diverse settings. Bangla, the world's sixth most spoken language, exemplifies this gap: existing resources overwhelmingly targe...
The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interaction models capable of seamless conversations. Despite remarkable performance as general voice assistants, their performance in specialized domains remains underexplore...
How faithfulness of LLMs to provided context depends on how plausible they perceive the context to be (context-memory conflict) is analyzed, which makes use of the increased difficulty of non-English and low-resource language text generation and input data based on local knowledge.
This work proposes Vague2Detect, a hybrid pipeline in which a fine-tuned Sentence-BERT retrieves candidates from a structured household Knowledge Base, and YOLO-World verifies their presence in the image, and improves performance to 61% VPSR with high precision, and up to 85% when augmented with GPT fallback.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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