150 citations · 326 across the 15 of their papers we have counts for
7 papers · 1 filter
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Baolin Peng, Michel Galley, Pengcheng He +8
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. Howev…
KAT: A Knowledge Augmented Transformer for Vision-and-Language
Liangke Gui, Borui Wang, Qiuyuan Huang +3
The primary focus of recent work with largescale transformers has been on optimizing the amount of information packed into the model's parameters. In this work, we ask a different…
Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural Representations
Kezhen Chen, Qiuyuan Huang, Hamid Palangi +3
Generating formal-language programs represented by relational tuples, such as Lisp programs or mathematical operations, to solve problems stated in natural language is a challengin…
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning
Ming Jiang, Junjie Hu, Qiuyuan Huang +3
Popular metrics used for evaluating image captioning systems, such as BLEU and CIDEr, provide a single score to gauge the system's overall effectiveness. This score is often not in…
TIGEr: Text-to-Image Grounding for Image Caption Evaluation
Ming Jiang, Qiuyuan Huang, Lei Zhang +5
This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems. Popular metrics, such as BLEU and CIDEr, are based solely on text matching b…
Attentive Tensor Product Learning
Qiuyuan Huang, Li Deng, Dapeng Wu +2
This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models. ATPL is a new architecture to bridg…
A Neural-Symbolic Approach to Design of CAPTCHA
Qiuyuan Huang, Paul Smolensky, Xiaodong He +2
CAPTCHAs based on reading text are susceptible to machine-learning-based attacks due to recent significant advances in deep learning (DL). To address this, this paper promotes imag…