Unmasking Hallucinations: A Causal Graph-Attention Perspective on Factual Reliability in Large Language Models
This paper primarily focuses on the hallucinations caused due to AI language models(LLMs).LLMs have shown extraordinary Language understanding and generation capabilities .Still it has major a disadvantage hallucinations which give outputs which are factually incorrect ,misleading or unsupported by input data . These hallucinations cause serious problems in scenarios like medical diagnosis or legal reasoning.Through this work,we propose causal graph attention network (GCAN) framework that reduces hallucinations through interpretation of internal attention flow within a transformer architecture with the help of constructing token level graphs that combine self attention weights and gradien...
| Authors | Sailesh kiran kurra, Shiek Ruksana, Vishal Borusu |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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