LLM2LLM: UC Berkeley, ICSI and LBNL Researchers’ Innovative Approach to Boosting Large Language Model Performance in Low-Data Regimes with Synthetic Data

Large language models (LLMs) are at the forefront of technological advancements in natural language processing, marking a significant leap in the ability of machines to understand, interpret, and generate human-like text. However, the full potential of LLMs often remains untapped due to the limitations imposed by the scarcity of specialized, task-specific training data. This bottleneck […]

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