1.1k citations · 1.9k across the 47 of their papers we have counts for
10 papers · 1 filter
DePlot: One-shot visual language reasoning by plot-to-table translation
Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno +7
Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models req…
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Xichen Pan, Pengda Qin, Yuhong Li +2
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-worl…
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang +1
Recently, there has been significant progress in teaching language models to perform step-by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting…
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Wenhu Chen, Hexiang Hu, Xi Chen +2
While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and even…
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…
Large Language Models are few(1)-shot Table Reasoners
Wenhu Chen
Recent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table rea…
Controllable Dialogue Simulation with In-Context Learning
Zekun Li, Wenhu Chen, Shiyang Li +3
Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper,…
Re-Imagen: Retrieval-Augmented Text-to-Image Generator
Wenhu Chen, Hexiang Hu, Chitwan Saharia +1
Research on text-to-image generation has witnessed significant progress in generating diverse and photo-realistic images, driven by diffusion and auto-regressive models trained on…
QA Is the New KR: Question-Answer Pairs as Knowledge Bases
Wenhu Chen, William W. Cohen, Michiel De Jong +4
In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed…
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering
Wenhu Chen, Pat Verga, Michiel de Jong +2
Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large n…