A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
This addresses a bottleneck in using LLMs for structured information extraction, offering an incremental improvement for applications requiring precise output formats.
The paper tackled the problem of inconsistent structured text generation by large language models (LLMs) in tasks like named entity recognition and relation extraction, introducing a two-step pipeline method (G&O) that improved LLM performance with minimal additional effort.
Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsistent when tasked with producing text that adheres to specific structured formats, which is crucial in applications like named entity recognition (NER) or relation extraction (RE). To address this issue, this paper introduces an efficient method, G&O, to enhance their structured text generation capabilities. It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses. Subsequently, LLMs are asked to organize the output into the desired structure, using the intermediate responses as context. G&O effectively separates the generation of content from the structuring process, reducing the pressure of completing two orthogonal tasks simultaneously. Tested on zero-shot NER and RE, the results indicate a significant improvement in LLM performance with minimal additional efforts. This straightforward and adaptable prompting technique can also be combined with other strategies, like self-consistency, to further elevate LLM capabilities in various structured text generation tasks.