3 accepted papers
The increasing size of large language models (LLMs) poses significant challenges due to their high computational costs and energy consumption, making their deployment in industrial settings difficult. Small language models (SLMs) have been introduced to mitigate these challenges by reducing model si…
Large language models (LLMs) have achieved notable success across various tasks but are hindered by their large size and high computational demands. Post-training pruning (PTP) offers a promising solution by reducing model size through parameter removal while preserving performance. However, current…
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to the difficulty of the problem. In recent years, in machine learning, Franceschi et al. have proposed a method for solvi…