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Found 9 papers, 6 papers with code
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Recent work has studied prompt-driven synthetic data generation using large language models, but these generated datasets tend to lack complexity and diversity.
Measuring Adversarial Datasets
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 errors.
Prompt2Model: Generating Deployable Models from Natural Language Instructions
In this paper, we propose Prompt2Model, a general-purpose method that takes a natural language task description like the prompts provided to LLMs, and uses it to train a special-purpose model that is conducive to deployment.
Large Language Models Enable Few-Shot Clustering
In this paper, we ask whether a large language model can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering.
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
Using this data, we compare various information retrieval algorithms on our test set and present a superior bi-encoder retriever for text-based dataset recommendation.
A Dataset for N-ary Relation Extraction of Drug Combinations
Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.
DataLab: A Platform for Data Analysis and Intervention
Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding
Our splits identify performance gaps up to 10% between end-to-end systems that were within 1% of each other on the original test sets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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CitationIE: Leveraging the Citation Graph for Scientific Information Extraction
Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress.
