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pro vyhledávání: '"Dekoninck, Jasper"'
Public benchmarks play an essential role in the evaluation of large language models. However, data contamination can lead to inflated performance, rendering them unreliable for model comparison. It is therefore crucial to detect contamination and est
Externí odkaz:
http://arxiv.org/abs/2405.16281
Large language models are widespread, with their performance on benchmarks frequently guiding user preferences for one model over another. However, the vast amount of data these models are trained on can inadvertently lead to contamination with publi
Externí odkaz:
http://arxiv.org/abs/2402.02823
As Large Language Models (LLMs) are deployed more widely, customization with respect to vocabulary, style, and character becomes more important. In this work, we introduce model arithmetic, a novel inference framework for composing and biasing LLMs w
Externí odkaz:
http://arxiv.org/abs/2311.14479