Sang Truong

All Publications

• Red teaming ChatGPT in medicine to yield real-world insights on model behavior. NPJ digital medicine Chang, C. T., Farah, H., Gui, H., Rezaei, S. J., Bou-Khalil, C., Park, Y. J., Swaminathan, A., Omiye, J. A., Kolluri, A., Chaurasia, A., Lozano, A., Heiman, A., Jia, A. S., Kaushal, A., Jia, A., Iacovelli, A., Yang, A., Salles, A., Singhal, A., Narasimhan, B., Belai, B., Jacobson, B. H., Li, B., Poe, C. H., Sanghera, C., Zheng, C., Messer, C., Kettud, D. V., Pandya, D., Kaur, D., Hla, D., Dindoust, D., Moehrle, D., Ross, D., Chou, E., Lin, E., Haredasht, F. N., Cheng, G., Gao, I., Chang, J., Silberg, J., Fries, J. A., Xu, J., Jamison, J., Tamaresis, J. S., Chen, J. H., Lazaro, J., Banda, J. M., Lee, J. J., Matthys, K. E., Steffner, K. R., Tian, L., Pegolotti, L., Srinivasan, M., Manimaran, M., Schwede, M., Zhang, M., Nguyen, M., Fathzadeh, M., Zhao, Q., Bajra, R., Khurana, R., Azam, R., Bartlett, R., Truong, S. T., Fleming, S. L., Raj, S., Behr, S., Onyeka, S., Muppidi, S., Bandali, T., Eulalio, T. Y., Chen, W., Zhou, X., Ding, Y., Cui, Y., Tan, Y., Liu, Y., Shah, N., Daneshjou, R. 2025; 8 (1): 149

  Abstract

  Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened teams of clinicians, medical and engineering students, and technical professionals (80 participants total) to stress-test models with real-world clinical cases and categorize inappropriate responses along axes of safety, privacy, hallucinations/accuracy, and bias. Six medically-trained reviewers re-analyzed prompt-response pairs and added qualitative annotations. Of 376 unique prompts (1504 responses), 20.1% were inappropriate (GPT-3.5: 25.8%; GPT-4.0: 16%; GPT-4.0 with Internet: 17.8%). Subsequently, we show the utility of our benchmark by testing GPT-4o, a model released after our event (20.4% inappropriate). 21.5% of responses appropriate with GPT-3.5 were inappropriate in updated models. We share insights for constructing red teaming prompts, and present our benchmark for iterative model assessments.

  View details for DOI 10.1038/s41746-025-01542-0

  View details for PubMedID 40055532

  View details for PubMedCentralID 10564921

• Systematic analysis of biomolecular conformational ensembles with PENSA. The Journal of chemical physics Vogele, M., Thomson, N. J., Truong, S. T., McAvity, J., Zachariae, U., Dror, R. O. 2025; 162 (1)

  Abstract

  Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several conditions-for example, with various mutations or bound ligands-in order to identify differences between the molecular conformations adopted under these conditions. However, the large amount of data produced by simulations of ever larger and more complex systems often renders it difficult to identify the structural features that are relevant to a particular biochemical phenomenon. We present a flexible software package named Python ENSemble Analysis (PENSA) that enables a comprehensive and thorough investigation into biomolecular conformational ensembles. It provides featurization and feature transformations that allow for a complete representation of biomolecules such as proteins and nucleic acids, including water and ion binding sites, thus avoiding the bias that would come with manual feature selection. PENSA implements methods to systematically compare the distributions of molecular features across ensembles to find the significant differences between them and identify regions of interest. It also includes a novel approach to quantify the state-specific information between two regions of a biomolecule, which allows, for example, tracing information flow to identify allosteric pathways. PENSA also comes with convenient tools for loading data and visualizing results, making them quick to process and easy to interpret. PENSA is an open-source Python library maintained at https://github.com/drorlab/pensa along with an example workflow and a tutorial. We demonstrate its usefulness in real-world examples by showing how it helps us determine molecular mechanisms efficiently.

  View details for DOI 10.1063/5.0235544

  View details for PubMedID 39745157

• Bayesian Optimization for Crop Genetics with Scalable Probabilistic Models Azam, R., Truong, S. T., Fernandes, S. B., Leakey, A. D. B., Lipka, A., El-Kebir, M., Koyejo, S. edited by Antoran, J., Naesseth, C. A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024: 30-44

  View details for Web of Science ID 001347127100002

• Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models Truong, S. T., Nguyen, D. Q., Toan Nguyen, Le, D. D., Truong, N. N., Tho Quan, Koyejo, S. edited by Duh, K., Gomez, H., Bethard, S. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2024: 2849-2900

  View details for Web of Science ID 001511618600182

• An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models Bhatt, G., Chen, Y., Das, A. M., Zhang, J., Truong, S. T., Mussmann, S., Zhu, Y., Bilmes, J., Du, S. S., Jamieson, K., Ash, J. T., Nowak, R. D. edited by Martins, A., Srikumar, Ku, L. W. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2024: 6549-6560

  View details for Web of Science ID 001356731806041

• DECODINGTRUST: A Comprehensive Assessment of Trustworthiness in GPT Models Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., Truong, S. T., Arora, S., Mazeika, M., Hendrycks, D., Lin, Z., Cheng, Y., Koyejo, S., Song, D., Li, B. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001220600008008

• GAUCHE: A Library for Gaussian Processes in Chemistry Griffiths, R., Klarner, L., Moss, H., Ravuri, A., Truong, S., Stanton, S., Tom, G., Rankovic, B., Du, Y., Jamasb, A., Deshwal, A., Schwartz, J., Tripp, A., Kell, G., Frieder, S., Bourached, A., Chan, A. J., Moss, J., Guo, C., Durholt, J., Chaurasia, S., Park, J., Strieth-Kalthoff, F., Lee, A. A., Cheng, B., Aspuru-Guzik, A., Schwaller, P., Tang, J. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001224281501021