Zobrazeno 1 - 10
of 2 440
pro vyhledávání: '"Golebiowski A"'
Autor:
Schneider, Lennart, Wistuba, Martin, Klein, Aaron, Golebiowski, Jacek, Zappella, Giovanni, Merra, Felice Antonio
Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks. As the most powerful models are proprietary and can only be invoked via an API, users often manually refine prompts in a black-box setting
Externí odkaz:
http://arxiv.org/abs/2412.07820
Autor:
Gołębiowski, Krzysztof
The main aim of this article is to prove that for any continuous function $f \colon X \to X$, where $X$ is metrizable (or, more generally, for any family $\mathcal{F}$ of such functions, satisfying an additional condition), there exists a compatible
Externí odkaz:
http://arxiv.org/abs/2412.03711
Techniques for knowledge graph (KGs) enrichment have been increasingly crucial for commercial applications that rely on evolving product catalogues. However, because of the huge search space of potential enrichment, predictions from KG completion (KG
Externí odkaz:
http://arxiv.org/abs/2406.07098
Autor:
Kästner, Linh, Shcherbyna, Volodymyir, Zeng, Huajian, Le, Tuan Anh, Schreff, Maximilian Ho-Kyoung, Osmaev, Halid, Tran, Nam Truong, Diaz, Diego, Golebiowski, Jan, Soh, Harold, Lambrecht, Jens
Publikováno v:
Robotics Science and Systems 2024, Delft Netherlands
Building upon our previous contributions, this paper introduces Arena 3.0, an extension of Arena-Bench, Arena 1.0, and Arena 2.0. Arena 3.0 is a comprehensive software stack containing multiple modules and simulation environments focusing on the deve
Externí odkaz:
http://arxiv.org/abs/2406.00837
Pre-trained language models (PLM), for example BERT or RoBERTa, mark the state-of-the-art for natural language understanding task when fine-tuned on labeled data. However, their large size poses challenges in deploying them for inference in real-worl
Externí odkaz:
http://arxiv.org/abs/2405.02267
Large language models (LLMs) encode vast amounts of world knowledge. However, since these models are trained on large swaths of internet data, they are at risk of inordinately capturing information about dominant groups. This imbalance can propagate
Externí odkaz:
http://arxiv.org/abs/2310.14777
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. OPE is a viable alternative to running expensive online A/B tests: it can speed up the development of ne
Externí odkaz:
http://arxiv.org/abs/2305.03954
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capt
Externí odkaz:
http://arxiv.org/abs/2305.03623
Autor:
Wang, Cheng, Golebiowski, Jacek
Model miscalibration has been frequently identified in modern deep neural networks. Recent work aims to improve model calibration directly through a differentiable calibration proxy. However, the calibration produced is often biased due to the binnin
Externí odkaz:
http://arxiv.org/abs/2303.15057
Publikováno v:
In The Ocular Surface October 2024 34:381-391
