Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Tang, Raphael"'
Autor:
Li, Wenyan, Zhang, Xinyu, Li, Jiaang, Peng, Qiwei, Tang, Raphael, Zhou, Li, Zhang, Weijia, Hu, Guimin, Yuan, Yifei, Søgaard, Anders, Hershcovich, Daniel, Elliott, Desmond
Food is a rich and varied dimension of cultural heritage, crucial to both individuals and social groups. To bridge the gap in the literature on the often-overlooked regional diversity in this domain, we introduce FoodieQA, a manually curated, fine-gr
Externí odkaz:
http://arxiv.org/abs/2406.11030
Autor:
Tang, Raphael, Zhang, Xinyu, Xu, Lixinyu, Lu, Yao, Li, Wenyan, Stenetorp, Pontus, Lin, Jimmy, Ture, Ferhan
Diffusion models are the state of the art in text-to-image generation, but their perceptual variability remains understudied. In this paper, we examine how prompts affect image variability in black-box diffusion-based models. We propose W1KP, a human
Externí odkaz:
http://arxiv.org/abs/2406.08482
Recent advances in retrieval-augmented models for image captioning highlight the benefit of retrieving related captions for efficient, lightweight models with strong domain-transfer capabilities. While these models demonstrate the success of retrieva
Externí odkaz:
http://arxiv.org/abs/2406.02265
Customer service is how companies interface with their customers. It can contribute heavily towards the overall customer satisfaction. However, high-quality service can become expensive, creating an incentive to make it as cost efficient as possible
Externí odkaz:
http://arxiv.org/abs/2405.00801
Listwise rerankers based on large language models (LLM) are the zero-shot state-of-the-art. However, current works in this direction all depend on the GPT models, making it a single point of failure in scientific reproducibility. Moreover, it raises
Externí odkaz:
http://arxiv.org/abs/2312.02969
Recent prompt optimisation approaches use the generative nature of language models to produce prompts -- even rivaling the performance of human-curated prompts. In this paper, we demonstrate that randomly sampling tokens from the model vocabulary as
Externí odkaz:
http://arxiv.org/abs/2311.09569
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking. To address this, we propose permutation self-consistency, a form of self-consistency over ranking list outputs of black-box L
Externí odkaz:
http://arxiv.org/abs/2310.07712
Deep neural networks (DNNs) are often used for text classification tasks as they usually achieve high levels of accuracy. However, DNNs can be computationally intensive with billions of parameters and large amounts of labeled data, which can make the
Externí odkaz:
http://arxiv.org/abs/2212.09410
Autor:
Tang, Raphael, Kumar, Karun, Yang, Gefei, Pandey, Akshat, Mao, Yajie, Belyaev, Vladislav, Emmadi, Madhuri, Murray, Craig, Ture, Ferhan, Lin, Jimmy
End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweight computation for inference. Of course, this impedes commercialization
Externí odkaz:
http://arxiv.org/abs/2211.11740
Autor:
Tang, Raphael, Liu, Linqing, Pandey, Akshat, Jiang, Zhiying, Yang, Gefei, Kumar, Karun, Stenetorp, Pontus, Lin, Jimmy, Ture, Ferhan
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this paper, we perform a text-image attribution analysis on Stable Diffusion
Externí odkaz:
http://arxiv.org/abs/2210.04885