Quantifying out-of-distribution data severity with Wasserstein distances; benchmarking large language models for alloy property prediction
Read the original at arxiv.org→arXiv:2611.03767v1 Announce Type: new Abstract: As general-purpose Large Language Models (LLMs), such as Gemini and DeepSeek, exhibit increasingly advanced reasoning capabilities, a critical question arises: how do...
Original headline: "Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction"
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- Nov 4, 05:00 UTC arXiv cs.LG lead source Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction