Gyan: An Explainable Neuro-Symbolic Language Model
Transformer based pre-trained large language models have become ubiquitous. There is increasing evidence to suggest that even with large scale pre-training, these models do not capture complete compositional context and certainly not, the full human analogous context. Besides, by the very nature of the architecture, these models hallucinate, are difficult to maintain, are not easily interpretable and require enormous compute resources for training and inference. Here, we describe Gyan, an explainable language model based on a novel non-transformer architecture, without any of these limitations. Gyan achieves SOTA performance on 3 widely cited data sets and superior performance on two prop...
| Authors | Venkat Srinivasan, Vishaal Jatav, Anushka Chandrababu et al. |
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| Primary category | cs.CL |
| Published | 6 May 2026 |
| Last updated at source | 13 May 2026 |
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