Automated Molecular Concept Generation and Labeling with Large Language Models: An Evidence Synthesis on Resource Efficiency And Performance Trade-Offs

Nolan Scott; Preston Peterson; Reid Richardson

Abstract

This evidence-synthesis article examines language-centered multimodal learning through the focal contribution “Automated Molecular Concept Generation and Labeling with Large Language Models” and nine author-disjoint, topically matched studies. The analysis is organized around resource efficiency and performance trade-offs. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.

Keywords

language-centered multimodal learningresource efficiency and performance trade-offsevidence synthesisreproducibilityresearch evaluation

References

Zhang, Z., Wu, Q., Xia, B., Sun, F., Hu, Z., Sun, Y., & Zhang, S. (2025). Automated Molecular Concept Generation and Labeling with Large Language Models. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 6918–6936).
Fan, Y., & Strube, M. (2025). Consistent Discourse-level Temporal Relation Extraction Using Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2025, 18605-18622. https://doi.org/10.18653/v1/2025.findings-emnlp.1010 DOI
He, W., Ma, H., Li, S., Dong, H., Zhang, H., & Feng, J. (2023). Using Augmented Small Multimodal Models to Guide Large Language Models for Multimodal Relation Extraction. Applied Sciences, 13(22), 12208. https://doi.org/10.3390/app132212208 DOI
HE, L., WANG, R., DUAN, J., WANG, H., & LI, X. (2026). LLM-VGA: Large Language Model-Augmented Visual Generation with Hierarchical Bidirectional Alignment for Multimodal Relation Extraction. IEICE Transactions on Information and Systems. https://doi.org/10.1587/transinf.2025edp7173 DOI
Li, Z., Pang, N., & Zhao, X. (2024). Instruction Tuning Large Language Models for Multimodal Relation Extraction Using LoRA. Lecture Notes in Computer Science, 364-376. https://doi.org/10.1007/978-981-97-7707-5_30 DOI
Aidynkyzy, A. (2026). FROM NAMED ENTITY RECOGNITION TO RELATION EXTRACTION: LARGE LANGUAGE MODEL ASSISTED CONSTRUCTION OF THE KAZAKH RELATION EXTRACTION DATASET. Вестник Академии гражданской авиации, 41(2). https://doi.org/10.53364/24138614_2026_41_2_13 DOI
Liu, B., & Qi, G. (2025). LLM-CG: Large language model-enhanced constraint graph for distantly supervised relation extraction. Neurocomputing, 655, 131426. https://doi.org/10.1016/j.neucom.2025.131426 DOI
Zhao, H., Yilahun, H., & Hamdulla, A. (2023). Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction. 2023 International Conference on Asian Language Processing (IALP), 31-36. https://doi.org/10.1109/ialp61005.2023.10337264 DOI
Choi, G., & Kim, H. (2025). Automated Claim–Evidence Extraction for Political Discourse Analysis: A Large Language Model Approach to Rodong Sinmun Editorials. Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER), 1-17. https://doi.org/10.18653/v1/2025.fever-1.1 DOI
Shah, P. (2025). Multimodal Large Language Model (MLLM) Noise Resistance. . https://doi.org/10.33774/coe-2025-6z9zx DOI

Publication details

Journal: Machine Intelligence & Responsible Systems
Volume: 1 (2026)
Article number: mi20260007
License: CC BY 4.0

[1] Zhang, Z., Wu, Q., Xia, B., Sun, F., Hu, Z., Sun, Y., & Zhang, S. (2025). Automated Molecular Concept Generation and Labeling with Large Language Models. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 6918–6936).

[2] Fan, Y., & Strube, M. (2025). Consistent Discourse-level Temporal Relation Extraction Using Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2025, 18605-18622. https://doi.org/10.18653/v1/2025.findings-emnlp.1010 DOI

[3] He, W., Ma, H., Li, S., Dong, H., Zhang, H., & Feng, J. (2023). Using Augmented Small Multimodal Models to Guide Large Language Models for Multimodal Relation Extraction. Applied Sciences, 13(22), 12208. https://doi.org/10.3390/app132212208 DOI

[4] HE, L., WANG, R., DUAN, J., WANG, H., & LI, X. (2026). LLM-VGA: Large Language Model-Augmented Visual Generation with Hierarchical Bidirectional Alignment for Multimodal Relation Extraction. IEICE Transactions on Information and Systems. https://doi.org/10.1587/transinf.2025edp7173 DOI

[5] Li, Z., Pang, N., & Zhao, X. (2024). Instruction Tuning Large Language Models for Multimodal Relation Extraction Using LoRA. Lecture Notes in Computer Science, 364-376. https://doi.org/10.1007/978-981-97-7707-5_30 DOI

[6] Aidynkyzy, A. (2026). FROM NAMED ENTITY RECOGNITION TO RELATION EXTRACTION: LARGE LANGUAGE MODEL ASSISTED CONSTRUCTION OF THE KAZAKH RELATION EXTRACTION DATASET. Вестник Академии гражданской авиации, 41(2). https://doi.org/10.53364/24138614_2026_41_2_13 DOI

[7] Liu, B., & Qi, G. (2025). LLM-CG: Large language model-enhanced constraint graph for distantly supervised relation extraction. Neurocomputing, 655, 131426. https://doi.org/10.1016/j.neucom.2025.131426 DOI

[8] Zhao, H., Yilahun, H., & Hamdulla, A. (2023). Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction. 2023 International Conference on Asian Language Processing (IALP), 31-36. https://doi.org/10.1109/ialp61005.2023.10337264 DOI

[9] Choi, G., & Kim, H. (2025). Automated Claim–Evidence Extraction for Political Discourse Analysis: A Large Language Model Approach to Rodong Sinmun Editorials. Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER), 1-17. https://doi.org/10.18653/v1/2025.fever-1.1 DOI

[10] Shah, P. (2025). Multimodal Large Language Model (MLLM) Noise Resistance. . https://doi.org/10.33774/coe-2025-6z9zx DOI