SEED uses these generated modules to process most of the data records and dynamically decides when the LLM should step in to directly process some individual records, possibly using the data-access modules to retrieve relevant information from the data sources to assist the LLM in solving the task.
In addition to the row-based architecture, we introduce several techniques: cell-aware position embedding, teacher-student training paradigm, and selective backward to improve the performance of RoTaR model.
Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks.
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
We introduce a method for improving the structural understanding abilities of language models.
Ranked #1 on Open Information Extraction on Penn Treebank
Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design.
We cast a suite of information extraction tasks into a text-to-triple translation framework.
Ranked #1 on Open Information Extraction on OIE2016 (using extra training data)