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

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 prediction into extraction for tokens already present in the context.Specifically, our proposed next tokens extraction (NTE) paradigm learns a versatile IE model, Cuckoo 1 , with 102.6M 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 pretrained 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. 2