# On the Performance of an Explainable Language Model on PubMedQA

arXiv: https://arxiv.org/abs/2504.05074 · HTML: https://mlsanity.com/paper/2504.05074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 139 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions](https://mlsanity.com/paper/2404.03264/) (2024)
- [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Neuro-Symbolic AI for Military Applications](https://mlsanity.com/paper/2408.09224/) (2024)
- [Optimizing National Security Strategies through LLM-Driven Artificial Intelligence Integration](https://mlsanity.com/paper/2305.13927/) (2023)
- [Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme](https://mlsanity.com/paper/2003.01149/) (2020)
- [Blockchain-Enabled Management Framework for Federated Coalition Networks](https://mlsanity.com/paper/2503.09666/) (2025)

## Built on by (most cited first, 2 of 2)

- [Reasoning LLMs in the Medical Domain: A Literature Survey](https://mlsanity.com/paper/2508.19097/) (2025)
- [CURE: Confidence-driven Unified Reasoning Ensemble Framework for Medical Question Answering](https://mlsanity.com/paper/2510.14353/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have shown significant abilities in retrieving medical knowledge, reasoning over it and answering medical questions comparably to physicians. However, these models are not interpretable, hallucinate, are difficult to maintain and require enormous compute resources for training and inference. In this paper, we report results from Gyan, an explainable language model based on an alternative architecture, on the PubmedQA data set. The Gyan LLM is a compositional language model and the model is decoupled from knowledge. Gyan is trustable, transparent, does not hallucinate and does not require significant training or compute resources. Gyan is easily transferable across domains. Gyan-4.3 achieves SOTA results on PubmedQA with 87.1% accuracy compared to 82% by MedPrompt based on GPT-4 and 81.8% by Med-PaLM 2 (Google and DeepMind). We will be reporting results for other medical data sets - MedQA, MedMCQA, MMLU - Medicine in the future.