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Ascertain the relationship between hallucination and the spin glass solution space

Determine whether and how hallucination in large language models is intimately related to the solution space of the spin glass model corresponding to a single-layer linear-attention transformer trained on linear regression tasks, particularly through spurious states in an associative memory model under fixed training dataset complexity.

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Background

Beyond chain-of-thought prompting, the authors explicitly highlight hallucination—where models generate outputs that are not grounded in factual knowledge—as a challenging phenomenon to be understood.

They speculate a connection between hallucination and the solution space (including spurious states) of their spin glass model and explicitly identify these as open questions to be addressed.

References

Future exciting directions include explaining the chain-of-thought prompting, i.e., decomposition of a complex task into intermediate steps, and more challenging case of hallucination, i.e., the model could not distinguish the generated outputs from factual knowledge, or it could not understand what they generate. We speculate that this hallucination may be intimately related to the solution space of the spin glass model given a fixed complexity of training dataset, e.g., spurious states in a standard associative memory model, as implied by Eq.~eq:Hamiltonian. These open questions are expected to be addressed in the near future, thereby enhancing robustness and trustworthiness of AI systems.

— Spin glass model of in-context learning (2408.02288 - Li et al., 5 Aug 2024) in Conclusion