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mattmazur.com | ||
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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.quantamagazine.org
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| | | | | Quantas award-winning coverage of computational complexity, quantum computing, artificial intelligence, cryptography and more. | |
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blog.georgeshakan.com
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| | | | | In Machine Learning, and in particular Generative AI, temperature is a useful hyperparameter for tuning model outputs. In this post, we will discuss the following. Temperature is a parameter developers can use to alter outputs from Large Language Models With a higher temperature we get more creative outputs. Why changing the temperature is useful. Let's... | |
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seekinglavenderlane.com
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