Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM’s Nest

ACL ARR 2025 February Submission99 Authors

Abstract:

Massive high-quality data, both pre-training raw texts and post-training annotations, have been carefully prepared to incubate advanced large language models (LLMs).
In contrast, for information extraction (IE), pre-training data, such as BIO-tagged sequences, are hard to scale up.
We show that IE models can act as free riders on LLM resources by reframing next-token \emph{prediction} into \emph{extraction} for tokens already present in the context.
Specifically, our proposed next tokens extraction (NTE) paradigm learns a versatile IE model, \emph{Cuckoo}, with $102.6$M extractive data converted from LLM's pre-training and post-training data.
Under the few-shot setting, Cuckoo adapts effectively to traditional and complex instruction-following IE with better performance than existing pre-trained IE models.
As a free rider, Cuckoo can naturally evolve with the ongoing advancements in LLM data preparation, benefiting from improvements in LLM training pipelines without additional manual effort.

Paper Type: Long

Research Area: Information Extraction

Research Area Keywords: IE pre-training, Learning paradigm, Transfer learning

Contribution Types: Approaches to low-resource settings

Languages Studied: English

Submission Number: 99