arXiv:2605.30179v1 Announce Type: new Abstract: Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not expose the latent interactions that often drive scientific labels. We introduce iLoRA. To our knowledge, it is the first Bayesian graph-conditioned LoRA framework. It infers a latent interaction graph from the input and uses it to generate input-conditioned LoRA updates. As a result, iLoRA learns prediction and latent interaction structure jointly, rather than training a predictor and applying inter
The proliferation of Large Language Models (LLMs) across diverse scientific domains necessitates more parameter-efficient adaptation methods capable of uncovering hidden relationships in complex data.
This development represents a significant advancement in fine-tuning LLMs for specialized scientific applications, particularly where latent interactions are crucial for accurate diagnosis and understanding.
The ability to jointly learn prediction and latent interaction structures in biological data marks a qualitative leap in how AI can be applied to diagnostics, moving beyond static adaptations.
- · Biotech and pharmaceutical companies
- · Medical diagnostic developers
- · AI/ML researchers in scientific domains
- · Microbiome research institutions
- · Traditional, less adaptive AI diagnostic methods
- · Specialized researchers without access to advanced AI tools
Improved accuracy and explainability in microbiome diagnoses.
Accelerated discovery of new biomarkers and therapeutic targets based on identified latent interactions.
Enhanced development of personalized medicine strategies informed by individualized biological network analysis.
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Read at arXiv cs.LG