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Jeffrey Bigham

8 accepted papers

2024

DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation

ECCV 2024poster

"Enabling machines to understand structured visuals like slides and user interfaces is essential for making them accessible to people with disabilities. However, achieving such understanding computationally has required manual data collection and annotation, which is time-consuming and labor-intensi…

2024

UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback

NAACL 2024long

Many large language models (LLMs) struggle to consistently generate UI code that compiles and produces visually relevant designs. Existing approaches to improve generation rely either on expensive human feedback or distilling a proprietary model. In this paper, we explore the use of automated feedba…

Cited by 14SourcePDFScholar
2023

Downstream Datasets Make Surprisingly Good Pretraining Corpora

ACL 2023long

For most natural language processing tasks, the dominant practice is to finetune large pretrained transformer models (e.g., BERT) using smaller downstream datasets. Despite the success of this approach, it remains unclear to what extent these gainsare attributable to the massive background corpora e…

2022

InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

EMNLP 2022main

Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Dialogue is an especially interesting area in which to explore instruction tuning because dialogue systems perform multiple kind…

2022

Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation

NAACL 2022findings

Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence. Such control is useful for designing dialogue systems that direct a conversation toward specific goals, such as creating non-obtrusive recommendations or…

2021

Controlling Dialogue Generation with Semantic Exemplars

NAACL 2021long

Dialogue systems pretrained with large language models generate locally coherent responses, but lack fine-grained control over responses necessary to achieve specific goals. A promising method to control response generation is exemplar-based generation, in which models edit exemplar responses that a…

2021

Does Pretraining for Summarization Require Knowledge Transfer?

EMNLP 2021finding

Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization. While folk explanations suggest that knowledge transfer accounts for pretraining’s benefits, little is known about why it works or what makes a pretraining task or dataset suitable. In this paper,…

Cited by 45SourcePDFScholar
2021

Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

ACL 2021long

Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage…

Cited by 132SourcePDFScholar