AI-Powered Annotation Pipelines for Stabilizing Large Language Models: A Human-AI Synergy Approach

TL;DR

This paper introduces an AI-powered annotation pipeline that combines automated weak supervision with human validation to improve the stability, factual accuracy, and logical coherence of large language models, especially in sensitive industries.

Contribution

It presents a novel human-AI synergy approach for systematically identifying and fixing instability patterns in LLM outputs, enhancing robustness and reliability.

Findings

Improved model stability through targeted annotations

Enhanced factual correctness and logical coherence

Scalable human-AI feedback loop framework

Abstract

LLM implementations are failing in highly regulated industries owing to instability issues, inconsistent reasoning, hallucinations and performance variability, especially in workflows. These reliability issues restrict safe use of LLM in areas that need the precision of facts and consistent behavior (Aiyappa et al., 2023). The current methods of stabilization, such as, reinforcement learning with human feedback (RLHF) and supervised fine-tuning, offer quantifiable improvements but are expensive and based on the intensive annotation of humans, thus being not easily scaled in a sustainable way (Dong et al., 2023; Retzlaff et al., 2024). This paper presents an AI-based annotation pipeline that systematically identifies, labels, and fixes for instability patterns on LLM output. Our human-AI synergy method combines the models of automated weak supervision and confidence-based annotation with the target human validation to guarantee the reliability and moral uprightness of feedback information (Cabitza et al., 2023; Jiang et al., 2023). The semantic consistency, factual correctness, and logical coherence categories of stability-specific annotation are introduced into our framework, allowing the continuous calibration of models and the enhancement of their robustness based on the feedback loops (Honovich et al., 2021; Nan et al., 2021).