HFEPX · Eval paper review
Muhammad bin Javaid, Karim K. Ben Hicham, Enkhnyam Battulga, Alexander Mitsos +1 more
Published
Nov 8, 2026
Citations
0
Trust level
Low
Usefulness score
49/100 (Medium)
Extraction confidence
45% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Nov 8, 2026
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this as background context only. Do not make protocol decisions from this page alone.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
Best use
Secondary protocol comparison source
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Validate the evaluation procedure and quality controls in the full paper before operational use.
Main weakness
The available metadata is too thin to trust this as a primary source.
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
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 than a heuristic string-matching baseline at catching errors across related experiments. Deployed across 3,942 extracted records from 561 papers, the framework accepted 67.5% of them as written. Its proposed corrections concentrated on quantitative fields, where the extractor often conflated related experiments. We release the extracted dataset with the complete LLM judge audit logs, as well as the Chemistry Data Extractor Toolkit, an open-source application implementing this end-to-end pipeline in a code-free, browser-based environment for document parsing, customizable schema and prompt design, model-agnostic extraction and auditing, interactive provenance-highlighted review, and reproducible dataset bundling.
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
None explicit
No explicit feedback protocol extracted.
"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."
Human Eval, Llm As Judge, Automatic Metrics
Includes extracted eval setup.
"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."
Not reported
No explicit QC controls found.
"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."
Not extracted
No benchmark anchors detected.
"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."
Accuracy
Useful for evaluation criteria comparison.
"Prompt ablations show that requiring chunk-level citations not only provides provenance down to specific paragraphs and tables but also improves extraction accuracy."
No benchmark or dataset names were extracted from the available abstract.
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.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
No explicit human feedback protocol detected.
Evaluation mode is explicit
Detected: Human Eval, Llm As Judge, Automatic Metrics
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
Detected: accuracy
