2 citations · 5 across the 5 of their papers we have counts for
5 papers
ALR: A Retrieve-then-Reason Framework for Long-context Question Answering
Huayang Li, Pat Verga, Priyanka Sen +5
The context window of large language models (LLMs) has been extended significantly in recent years. However, while the context length that the LLM can process has grown, the capabi…
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
Chenyang Zhao, Xueying Jia, Vijay Viswanathan +2
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. However, prompting often leads models to make predic…
Measuring Adversarial Datasets
Yuanchen Bai, Raoyi Huang, Vijay Viswanathan +2
In the era of widespread public use of AI systems across various domains, ensuring adversarial robustness has become increasingly vital to maintain safety and prevent undesirable e…
Prompt2Model: Generating Deployable Models from Natural Language Instructions
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch +2
Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provi…
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
Vijay Viswanathan, Luyu Gao, Tongshuang Wu +2
Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly dif…