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Found 3 papers, 1 papers with code
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, where the model fabricates facts and produces non-factual statements.
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.
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