Zobrazeno 1 - 10
of 589
pro vyhledávání: '"Liu Yongkang"'
Text-to-SQL translates natural language queries into Structured Query Language (SQL) commands, enabling users to interact with databases using natural language. Essentially, the text-to-SQL task is a text generation task, and its development is prima
Externí odkaz:
http://arxiv.org/abs/2410.06011
Publikováno v:
ECAI2024
Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most existing methods depend on large-scale dialogue corpora and thus buildin
Externí odkaz:
http://arxiv.org/abs/2408.08724
Autor:
Liu, Yongkang, Nie, Ercong, Feng, Shi, Hua, Zheng, Ding, Zifeng, Wang, Daling, Zhang, Yifei, Schütze, Hinrich
Publikováno v:
2024 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient or entirely absent. To tackle this challenge, we propose a novel data
Externí odkaz:
http://arxiv.org/abs/2406.09881
Autor:
Liu, Yongkang, Zhang, Yiqun, Li, Qian, Liu, Tong, Feng, Shi, Wang, Daling, Zhang, Yifei, Schütze, Hinrich
Full-parameter fine-tuning has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance. As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU m
Externí odkaz:
http://arxiv.org/abs/2401.15207
LLMs (large language models) such as ChatGPT have shown remarkable language understanding and generation capabilities. Although reference-free evaluators based on LLMs show better human alignment than traditional reference-based evaluators, there are
Externí odkaz:
http://arxiv.org/abs/2305.14658
Autor:
Bai, Zhengwei, Wu, Guoyuan, Barth, Matthew J., Liu, Yongkang, Sisbot, Emrah Akin, Oguchi, Kentaro
Cooperative perception (CP) is attracting increasing attention and is regarded as the core foundation to support cooperative driving automation, a potential key solution to addressing the safety, mobility, and sustainability issues of contemporary tr
Externí odkaz:
http://arxiv.org/abs/2302.03128
We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the last hidden state to summarize the sequences, which makes models unable t
Externí odkaz:
http://arxiv.org/abs/2212.09086
Autor:
Bai, Zhengwei, Wu, Guoyuan, Barth, Matthew J., Liu, Yongkang, Sisbot, Emrah Akin, Oguchi, Kentaro
Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated percep
Externí odkaz:
http://arxiv.org/abs/2212.07060
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number o
Externí odkaz:
http://arxiv.org/abs/2210.13845
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks. However, current continual RL approaches overl
Externí odkaz:
http://arxiv.org/abs/2210.12301