Building and Auditing Chemical Datasets with LLMs: A General LLM-as-a-Judge Framework Applied to Homogeneous Catalytic PET Depolymerisation

Muhammad bin Javaid, Karim K. Ben Hicham, Enkhnyam Battulga, Alexander Mitsos, Martin Grohe

Large language models can rapidly extract structured multivariate experimental data from the chemistry literature, yet verifying thousands of generated records without ground truth remains difficult. We present a general, citation-grounded extraction and verification methodology centered on an independent "LLM-as-a-judge", applied to the literature on homogeneous catalytic poly(ethylene terephthalate) (PET) depolymerisation. In this pipeline, an extractor LLM generates schema-enforced records from research papers with citations to document chunks tagged with universally unique identifiers (UUIDs); an independent LLM judge then reads the full paper alongside each record, flags unsupported fields, and proposes cited corrections. Prompt ablations show that requiring chunk-level citations not only provides provenance down to specific paragraphs and tables but also improves extraction accuracy. Before deploying the judge at scale, we benchmarked it against manual evaluation on extractions from a hand-curated reference set of 295 ionic-liquid-catalysed PET glycolysis experiments. The judge agreed with human evaluation on 85.7% of reviewed records and was significantly more effective…