The Enterprise Language Model for Insurance (ELMI) provides insurers with the capabilities of generative AI (GenAI)
and LLMs safely, securely, cost-effectively and with the domain-specific knowledge that insurance processes require.
This guide is designed to empower insurance executives, technology decision makers and digital transformation teams with the knowledge they need to make informed decisions about incorporating GenAI and LLMs into their business processes.
In this work, we present a detailed quantitative comparison of LLM and traditional approaches (e.g. SVM) on the LexGLUE benchmark, which takes into account both performance (standard indices) and alternative metrics such as timing, power consumption and cost, in a word: the carbon-footprint.
Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer.
