3.3k citations · 5.6k across the 26 of their papers we have counts for
10 papers · 1 filter
CAME: Contrastive Automated Model Evaluation
Ru Peng, Qiuyang Duan, Haobo Wang +5
The Automated Model Evaluation (AutoEval) framework entertains the possibility of evaluating a trained machine learning model without resorting to a labeled testing set. Despite th…
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Liangyu Zha, Junlin Zhou, Liyao Li +22
Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made…
Towards Cross-Table Masked Pretraining for Web Data Mining
Chao Ye, Guoshan Lu, Haobo Wang +4
Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information. Given the recent influence of…
ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory
Chenxu Hu, Jie Fu, Chenzhuang Du +3
Large language models (LLMs) with memory are computationally universal. However, mainstream LLMs are not taking full advantage of memory, and the designs are heavily influenced by…
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Hao Chen, Yiming Zhang, Qi Zhang +5
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions. While instru…
Latent Processes Identification From Multi-View Time Series
Zenan Huang, Haobo Wang, Junbo Zhao +1
Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a.k.a.}, latent processes identification. Drive…
Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility
Wentao Ye, Mingfeng Ou, Tianyi Li +8
The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-s…
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
Shuai Zhao, Jinming Wen, Luu Anh Tuan +2
The prompt-based learning paradigm, which bridges the gap between pre-training and fine-tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot…
Better Sign Language Translation with Monolingual Data
Ru Peng, Yawen Zeng, Junbo Zhao
Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relie…
Controllable Textual Inversion for Personalized Text-to-Image Generation
Jianan Yang, Haobo Wang, Yanming Zhang +4
The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts. Text inversion (TI), alongsid…