Deploying models on target domain data subject to distribution shift requires adaptation.

Recent advances in large language models (LLMs) have accelerated research on automated optimization modeling. While real-world decision-making is inherently uncertain, most existing work has focused on deterministic optimization with known parameters, leaving the application of LLMs in uncertain settings largely unexplored. To that end, we propose the DAOpt framework including a new dataset OptU, a multi-agent decision-making module, and a simulation environment for evaluating LLMs with a focus on out-of-sample feasibility and robustness. Additionally, we enhance LLMs' modeling capabilities by incorporating few-shot learning with domain knowledge from stochastic and robust optimization.

Abstract--Dempster-Shafer Theory (DST) provides a powerful framework for modeling uncertainty and has been widely applied to multi-attribute classification tasks. However, traditional DST - based attribute fusion-based classifiers suffer from oversimplified membership function modeling and limited exploitation of the belief structure brought by basic probability assignment (BPA), reducing their effectiveness in complex real-world scenarios. This paper presents an enhanced attribute fusion-based classifier that addresses these limitations through two key innovations. First, we adopt a selective modeling strategy that utilizes both single Gaussian and Gaussian Mixture Models (GMMs) for membership function construction, with model selection guided by cross-validation and a tailored evaluation metric. Second, we introduce a novel method to transform the possibility distribution into a BPA by combining simple BPAs derived from normalized possibility distributions, enabling a much richer and more flexible representation of uncertain information. Furthermore, we apply the belief structure-based BPA generation method to the evidential K-Nearest Neighbors (EKNN) classifier, enhancing its ability to incorporate uncertainty information into decision-making. Comprehensive experiments on benchmark datasets are conducted to evaluate the performance of the proposed attribute fusion-based classifier and the enhanced evidential K-Nearest Neighbors classifier in comparison with both evidential classifiers and conventional machine learning classifiers. The results demonstrate that the proposed classifier outperforms the best existing evidential classifier, achieving an average accuracy improvement of 4.86%, while maintaining low variance, thus confirming its superior effectiveness and robustness.

We address your concern as follows. This clearly shows the advantage of our method. We answer your main questions as follows. Q1:"Do we need to commit ourselves to the OVR loss?...considering a loss function such as softmax cross entropy Y ou are absolutely correct! If convexity is not required (e.g., NN implementation), we can use more flexible multiclass loss and binary We will make this clear in the revision. Q2:"How to use the non-negative risk estimator in this problem?" We will add more elaborations about the formulation in the revision. Q3:"My question is have you tried different loss functions?" However, it does not converge in experiments. So we instead use sigmoid loss following Kiryo et al. [24]. Theorem 1 serves as a guide to choose binary loss for OVR scheme. Thus, a consistency guarantee (Theorem 1) is necessary. Thanks for the detailed review and helpful comments. We address your main concerns as follows. For the other minor issues, we will discuss in the paper and revise the paper according to your suggestions. We would like to revise the terminology in the revision if it is allowed. Q2:"Some of the claims made about prior work are not accurate.