Work

D2.1_Grounding_Survey

Year: 2026

Type: article

Abstract: As large language models (LLMs) continue to advance, grounding has emerged as a crucial requirement for building reliable and verifiable LLM systems. Grounding refers to connecting a system to real-world knowledge, context, and data within a specific application domain. This survey reviews state-of-the-art grounding mechanisms through three complementary lenses: knowledge grounding, training-time grounding, and bias and representation, taking into account where knowledge is integrated and how it is adequately represented lexically and semantically in the outputs. Aligned across these dimensions, our aim is to provide a comprehensive view of current approaches, their limitations, and outline open challenges for developing fair, transparent, and accountable LLM-driven systems.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23624062

Authors: Pahalage, Thushari, Liang, Wuyue, Carrino, Gabriele, Franceschi, Valeria, Rigotti, Mattia, Lissandrini, Matteo

Institutions: University of Verona, IBM Research - Zurich

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7223461205

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