Adaptive, Uncertainty-aware, Reliable, and Efficient Language Intelligence Systems
Advancing Language Intelligence Beyond Boundaries
Director: Dr. Benyamin Ahmadnia
AURELIS is a research laboratory dedicated to advancing adaptive, reliable, efficient, and uncertainty-aware language intelligence. The lab conducts research at the intersection of artificial intelligence, natural language processing, and language-centered intelligent systems, with an emphasis on developing computational methods that can learn, reason, communicate, and adapt across languages, domains, tasks, and resource conditions. AURELIS pursues both foundational and applied research, with the long-term goal of developing language technologies that are robust, trustworthy, resource-aware, and effective in complex real-world environments.
AURELIS organizes its research around the following interconnected areas spanning multilingual language intelligence, next-generation language models, reliable and efficient AI, rigorous evaluation, and real-world language technologies.
1. Multilingual and Low-Resource Language Intelligence
Developing language technologies that generalize across languages and resource conditions, with emphasis on multilingual learning, cross-lingual transfer, underrepresented languages, and data-efficient methods.
2. Neural Machine Translation and Next-Generation Translation Systems
Advancing translation beyond conventional NMT through LLM-based and hybrid architectures, domain adaptation, terminology-aware methods, constrained generation, synthetic data, and robust translation evaluation.
3. Large Language Models and Generative Language AI
Investigating the adaptation, reasoning, generation, and evaluation capabilities of large language models and emerging generative language systems across tasks, languages, and domains.
4. Reliable, Uncertainty-aware, and Trustworthy Language AI
Developing methods for uncertainty estimation, confidence calibration, robustness, hallucination and failure detection, factuality, and trustworthy behavior in language-based AI systems.
5. Adaptive and Resource-Efficient Language Models
Designing language systems that adapt efficiently to new tasks and environments through parameter-efficient learning, model specialization, adaptive inference, compression, and computation-aware methods.
6. Evaluation, Verification, and Controlled Language Generation
Developing rigorous methods for evaluating and verifying language systems, including semantic and factual fidelity, constraint satisfaction, provenance, terminology preservation, benchmark design, and controlled generation.
7. Human-Centered and Domain-Specialized Language AI
Translating advances in language intelligence into useful real-world systems, with applications in education, computing, healthcare, specialized domains, and human-AI collaboration.
AURELIS develops research projects that translate its core research areas into focused investigations, experimental systems, and student-led research with potential for broader scholarly and societal impact.