Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction

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 their property prediction abilities compare to those of domain-specific pretrained m…

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1. 2026-11-04 05:00 · arXiv cs.LG
   Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction