LLMs display high variance and major accuracy drops on GSM-Symbolic variants of grade-school math problems, indicating they replicate training patterns rather than execute logical reasoning.
Un- derstanding hallucinations in diffusion mod- els through mode interpolation.URL https://arxiv
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Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
Proposes a value-encoding framework to characterize and counter homogenization in LLMs by formalizing it via normativity from queer theory and introducing xeno-reproduction tasks from feminist theory, illustrated with a gender-bias experiment on Claude 3.5 Haiku.
A theoretical framework for parameter estimation in inverse problems shows inversion does not necessarily improve accuracy per the data processing inequality and reveals a vulnerability in domain generalization via the Double Meaning Theorem.
citing papers explorer
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GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
LLMs display high variance and major accuracy drops on GSM-Symbolic variants of grade-school math problems, indicating they replicate training patterns rather than execute logical reasoning.
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Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
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The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety
Proposes a value-encoding framework to characterize and counter homogenization in LLMs by formalizing it via normativity from queer theory and introducing xeno-reproduction tasks from feminist theory, illustrated with a gender-bias experiment on Claude 3.5 Haiku.
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On Inverse Problems, Parameter Estimation, and Domain Generalization
A theoretical framework for parameter estimation in inverse problems shows inversion does not necessarily improve accuracy per the data processing inequality and reveals a vulnerability in domain generalization via the Double Meaning Theorem.