LLM Chain Ensembles for Scalable and Accurate Data Annotation
This provides a practical solution for scalable and cost-effective data annotation in domains with scarce labeled data, though it is incremental in optimizing existing LLM methods.
The paper tackles the high cost of deploying large language models (LLMs) for data annotation by introducing an LLM chain ensemble that routes data based on uncertainty, achieving performance gains over individual models and substantial cost savings.
The ability of large language models (LLMs) to perform zero-shot classification makes them viable solutions for data annotation in rapidly evolving domains where quality labeled data is often scarce and costly to obtain. However, the large-scale deployment of LLMs can be prohibitively expensive. This paper introduces an LLM chain ensemble methodology that aligns multiple LLMs in a sequence, routing data subsets to subsequent models based on classification uncertainty. This approach leverages the strengths of individual LLMs within a broader system, allowing each model to handle data points where it exhibits the highest confidence, while forwarding more complex cases to potentially more robust models. Our results show that the chain ensemble method often exceeds the performance of the best individual model in the chain and achieves substantial cost savings, making LLM chain ensembles a practical and efficient solution for large-scale data annotation challenges.