HFEPX · Eval paper review
Ali Vosoughi, Ayoub Shahnazari, Yufeng Xi, Zeliang Zhang +3 more
Published
Jul 12, 2025
Citations
0
Trust level
Low
Usefulness score
0/100 (Low)
Extraction confidence
15% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Mar 10, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
Best use
Background context only
Use if you need
Background context only.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This paper looks adjacent to evaluation work, but not like a strong protocol reference.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering. The framework measures context assimilation, or how models use fixed, domain-specific supporting information during inference. The framework includes 217 expert-curated X-ray diffraction (XRD) questions covering fundamental to advanced crystallographic concepts, each evaluated under closed-book (without context) and open-book (with context) conditions, where the latter includes concise reference passages generated by GPT-4.5 and refined by crystallography experts. We benchmark 74 state-of-the-art LLMs and MLLMs, including GPT-4, GPT-5, O-series, LLaVA, LLaMA, QWEN, Mistral, and Gemini families, to quantify how different architectures and scales assimilate external knowledge. Results show that mid-sized models (7B--70B parameters) gain the most from contextual materials, while very large models often show saturation or interference and the largest relative gains appear in small and mid-sized models. Expert-reviewed materials provide significantly higher improvements than AI-generated ones even when token counts are matched, confirming that content quality, not quantity, drives performance. OPENXRD offers a reproducible diagnostic benchmark for assessing reasoning, knowledge integration, and guidance sensitivity in scientific domains, and provides a foundation for future multimodal and retrieval-augmented crystallography systems.
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.
"We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering."
None explicit
Validate eval design from full paper text.
"We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering."
Not reported
No explicit QC controls found.
"We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering."
Not extracted
No benchmark anchors detected.
"We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering."
Not extracted
No metric anchors detected.
"We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering."
Domain Experts
Helpful for staffing comparability.
"The framework includes 217 expert-curated X-ray diffraction (XRD) questions covering fundamental to advanced crystallographic concepts, each evaluated under closed-book (without context) and open-book (with context) conditions, where the latter includes concise reference passages generated by GPT-4.5 and refined by crystallography experts."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering.
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
No clear evaluation mode extracted.
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
No metric terms extracted.
