robustness ↔ Large Language Models

Relations (1)

related 0.50 — strongly supporting 5 facts

Large Language Models are evaluated for their robustness as a key performance metric [1], though defining this concept remains a significant challenge in the current landscape {fact:2, fact:4}. Research indicates that these models may lack robustness due to overfitting [2] and are subject to theoretical frameworks designed to investigate these fundamental limitations [3].

Facts (5)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 4 facts

claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).

claimWolf et al. (2023) introduced the 'behavior expectation bounds' theoretical framework to formally investigate the fundamental limitations of robustness in Large Language Models.

claimLarge Language Models may be overfitting to the specific artifacts of a test set rather than the underlying task, leading to a fundamental lack of robustness, according to Lunardi et al. (2025).

claimIn the current landscape of Large Language Models, definitions of robustness, fairness, and privacy are often ambiguous and lack simple closed-form mathematical representations compared to traditional machine learning.

Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org arXiv 1 fact

claimZhang et al. (2023) identified reliability in LLMs by examining tendencies regarding hallucination, truthfulness, factuality, honesty, calibration, robustness, and interpretability.