In a large language model handling multiple NLP tasks, negative interactions can occur, including task interference, bias amplification, context confusion, and challenges in resource allocation. Careful engineering and fine-tuning strategies are needed to balance task-specific performance without compromising overall language understanding. Regular monitoring and adaptation help address these challenges.