4 citations · 11 across the 16 of their papers we have counts for
5 papers · 1 filter
Evaluating Language Models as Synthetic Data Generators
Seungone Kim, Juyoung Suk, Xiang Yue +7
Given the increasing use of synthetic data in language model (LM) post-training, an LM's ability to generate high-quality data has become nearly as crucial as its ability to solve…
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…
Training Task Experts through Retrieval Based Distillation
Jiaxin Ge, Xueying Jia, Vijay Viswanathan +2
One of the most reliable ways to create deployable models for specialized tasks is to obtain an adequate amount of high-quality task-specific data. However, for specialized tasks,…
Synthetic Multimodal Question Generation
Ian Wu, Sravan Jayanthi, Vijay Viswanathan +4
Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of hi…
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Saumya Gandhi, Ritu Gala, Vijay Viswanathan +2
Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data…