Papers by Daniel Stanley Tan
Foundations of PEERS: Assessing LLM Role Performance in Educational Simulations (2025.acl-srw)
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Jasper Meynard Arana, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Daniel Stanley Tan, Erika Fille Legara, Christopher Monterola
| Challenge: | In education, peer instruction is widely recognized as an effective active learning strategy, but evaluations of PI are limited by logistical constraints and variability in classroom settings. |
| Approach: | They propose a simulation framework that integrates Agent-Based Modeling, Large Language Models, and Bayesian Knowledge Tracing to emulate student learning dynamics. |
| Outcome: | The proposed framework integrates Agent-Based Modeling, Large Language Models, and Bayesian Knowledge Tracing to emulate student learning dynamics in real classrooms. |
Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation (2025.acl-industry)
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Kristine Ann M. Carandang, Jasper Meynard Arana, Ethan Robert Casin, Christopher Monterola, Daniel Stanley Tan, Jesus Felix B. Valenzuela, Christian Alis
| Challenge: | Clinical note generation (CNG) tools are being developed to address extended working hours and healthcare provider fatigue. |
| Approach: | They evaluate the reliability of 12 open-weight and proprietary LLMs from Anthropic, Meta, Mistral, and OpenAI in CNG in terms of their ability to generate notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity) |
| Outcome: | The results show that the LLMs generated notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity) overall, Meta’s Llama 70B was the most reliable, followed by Mistral’s Small model. |