We focused on foundational work necessary to implement LMR. In the theory facet, the doctoral candidates took on problems such as jointly learning choice utilities and rationality, multiple criteria decision aiding, neuro-symbolic machine learning, and large language models. In the application facet, the focus was on the implementation of concrete algorithms which rely on LMR, including unsupervised learning approaches for structured data, novel embedding approaches for knowledge graphs, and neuro-symbolic learning. A key study tackled in this facet is the use of large language models for autoformalization with the aim of creating bridges between representations automatically. The applications we study in the third facet of the project pertain to critical infrastructures, content retrieval, recommmendations and ethics.
After a 6-month warm-up phase, the project welcomed its doctoral candidates and was promptly afloat. The timeliness of the topic targeted by the project has led to 7 accepted papers, partly at major venues including ECAI, ECML, CIKM, and ESANN. The main results achieved include the first validity guarantees in bridging between natural language and structured queries. Algorithmically, we were able to outperform the state of the art in multiple decision criteria aiding over real and synthetic data. Moreover, our generalization of embedding algorithms into degenerate Clifford algebras provably shows that embedding algorithms thought to be different are just different facets of the same coin. New parameterized grounding approaches increase the flexibility of interfacing between neural and symbolic representations. Finally, our new fairness measure ensure that our approaches can exploit contextual norms to mitigate some of the current limitations of machine learning systems.
