On NER annotation, retrieval-based RAG prompting usually outperforms random in-context learning, but the best LLMs still fall 3 to 20 F1 points below human annotators on hard datasets.
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
LLMs as Data Annotators: How Close Are We to Human Performance
On NER annotation, retrieval-based RAG prompting usually outperforms random in-context learning, but the best LLMs still fall 3 to 20 F1 points below human annotators on hard datasets.