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

• URL: http://arxiv.org/abs/2502.11275v1
• Date: Sun, 16 Feb 2025 21:32:20 GMT
• Title: Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's Nest
• Authors: Letian Peng, Zilong Wang, Feng Yao, Jingbo Shang,
• Abstract summary: We show that information extraction models can act as free riders on large language models (LLMs) resources.<n>We show that IE models can act as free riders on LLM resources by reframing next-token emphprediction into emphextraction for tokens already present in the context.<n>Specifically, our proposed next tokens extraction (NTE) paradigm learns a versatile IE model, emphCuckoo, with 102.6M extractive data converted from LLM's pre-training and post-training data.
• Score: 36.58490792678384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 102.6M 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 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.

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