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simons.berkeley.edu | ||
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afiodorov.github.io
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| | | | | I am tired of Americanism: consuming in the Anglosphere sometimes makes me feellike all movies are the same. America ... | |
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ssc.io
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| | | | | Data integration and cleaning have long been a key focus of the data management community. Recent research indicates the potential of large language models (LLMs) for such tasks. However, scaling and automating data wrangling with LLMs for real-world use cases poses additional challenges. Manual prompt engineering for example, is expensive and hard to operationalise, while full fine-tuning of LLMs incurs high compute and storage costs. Following up on previous work, we evaluate parameter-efficient fine-tuning (PEFT) methods for efficiently automating data wrangling with LLMs. We conduct a study of four popular PEFT methods on differently sized LLMs for ten benchmark tasks, where we find that PEFT methods achieve performance on-par with full fine-tuning, and that we can leverage small LLMs with negligible performance loss. However, even though such PEFT methods are parameter-efficient, they still incur high compute costs at training time and require labeled training data. We explore a zero-shot setting to further reduce deployment costs, and propose our vision for ZeroMatch, a novel approach to zero-shot entity matching. It is based on maintaining a large number of pretrained LLM variants from different domains and intelligently selecting an appropriate variant at inference time. | |
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www.gisagents.org
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| | | | | This blog is a research site focused around my interests in Geographical Information Science (GIS) and Agent-Based Modeling (ABM). | |
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lmsys.org
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| | | We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation ... | ||
