Urdu-ClimateGPT: Adapting LLM for Climate Data in Urdu Language

Climate change is increasingly recognized as a major global challenge that affects environmental systems, weather patterns, and human societies around the world. Rising global temperatures have been linked to more frequent extreme weather events and long-term shifts in climate patterns. Communicating climate information effectively, therefore, becomes essential, especially in a way that is accessible and inclusive. But languages like Urdu are under-represented in the sources of climate knowledge, thus leaving many communities with fewer reliable sources of climate knowledge. To address this issue, Urdu-ClimateGPT is introduced by this study, as a domain-adapted language model based on LLaMA 3.1, along with a retrieval-augmented conversational framework built around it. Domain specific fine-tuning is combined with retrieval-based grounding evidence by the system. This is an effort to make the hallucinatory responses less common and factual disalignments in the generated responses less frequent, in the context of conversations with climate-related topics. The model was evaluated on a held-out set of Urdu climate prompts, and compared to the baseline LLaMA 3.1 model. The findings reveal that Urdu-ClimateGPT outperforms in various automated evaluation metrics including: language fluency, domain-specific correctness, factual consistency, and response completeness. Overall, a normalized average score of 0.82 was achieved by the Urdu-ClimateGPT, whereas a score of 0.52 was scored by the baseline model. These results suggest that large language models for low-resource languages in specialized domains can be adapted, which is both feasible and beneficial. It is shown by the study that hallucination like behavior can be reduced by retrieval augmented architectures when evaluated using automated metrics. However, further evaluation by human experts will be necessary to determine the system’s factual reliability and its potential real-world impact.