Are Two LLMs Better Than One? A Student-Teacher Dual-Head LLMs Architecture for Pharmaceutical Content Optimization
This provides a practical solution for reliable quality control in high-stakes, compliance-critical industries like pharmaceuticals, though it is incremental in combining existing techniques.
The paper tackles the problem of quality control for pharmaceutical content generation by introducing LRBTC, a modular LLM/VLM-driven architecture with a Student-Teacher dual model and human-in-the-loop workflow. It achieves 83.0% F1 and 97.5% recall on AIReg-Bench, reducing missed violations by 5x compared to Gemini 2.5 Pro, and improves mean accuracy by 26.7% on CSpelling.
Large language models (LLMs) are increasingly used to create content in regulated domains such as pharmaceuticals, where outputs must be scientifically accurate and legally compliant. Manual quality control (QC) is slow, error prone, and can become a publication bottleneck. We introduce LRBTC, a modular LLM and vision language model (VLM) driven QC architecture covering Language, Regulatory, Brand, Technical, and Content Structure checks. LRBTC combines a Student-Teacher dual model architecture, human in the loop (HITL) workflow with waterfall rule filtering to enable scalable, verifiable content validation and optimization. On AIReg-Bench, our approach achieves 83.0% F1 and 97.5% recall, reducing missed violations by 5x compared with Gemini 2.5 Pro. On CSpelling, it improves mean accuracy by 26.7%. Error analysis further reveals that while current models are strong at detecting misspellings (92.5 recall), they fail to identify complex medical grammatical (25.0 recall) and punctuation (41.7 recall) errors, highlighting a key area for future work. This work provides a practical, plug and play solution for reliable, transparent quality control of content in high stakes, compliance critical industries. We also provide access to our Demo under MIT Licenses.