arXiv:2604.17289v2 Announce Type: replace Abstract: Supervised fine-tuning of large language models relies on human-annotated data, yet annotation pipelines routinely involve multiple crowdworkers of heterogeneous expertise. Standard practice aggregates labels via majority vote or simple averaging, discarding annotator identity and causing the model to absorb the errors of unreliable annotators directly into its parameters. We propose REALM, a method that jointly learns the model parameters and a scalar expertise value for each annotator entirely unsupervised, requiring no supervision beyond a
The increasing reliance on fine-tuning large language models with human-annotated data, coupled with the inherent variability in annotator quality, necessitates innovative solutions to improve model reliability and efficiency.
This development allows for more accurate and robust AI models by mitigating the impact of noisy data, directly addressing a core limitation in current LLM development and deployment.
The ability to automatically assess and incorporate annotator expertise directly into model training fundamentally changes how fine-tuning pipelines are designed and executed, leading to more reliable AI outputs.
- · AI developers
- · Companies using LLMs
- · Data annotation platforms
- · AI-driven product companies
- · Inefficient data annotation services
- · Companies relying on unvalidated fine-tuning processes
AI models will become more reliable and less susceptible to biases from low-quality training data.
The cost and time associated with generating high-quality labeled datasets could decrease as annotation efficiency improves.
This could accelerate the deployment of AI in sensitive applications where data quality and model reliability are paramount.
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Read at arXiv cs.LG