Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics across three generations of recursive text generation using three complementary evaluation frameworks: rule-based pattern matching, embedding based semantic similarity, and downstream task performance. Experiments with three initial bias levels (0.1, 0.3, 0.6) and four mitigation strategies reveal equilibrium dynamics rather than monotonic amplification. The low initial bias amplifies toward the model’s inherent bias level (+ 36%), whereas the high initial bias decays toward it (-26%). Among mitigation methods, contrastive augmentation, which introduces gender-swapped variants, achieves significant downstream bias reduction (98.8% for low initial bias and 91% on average) despite producing higher embedding-based bias scores. This paradox demonstrates that semantic similarity metrics may diverge from behavioral fairness outcomes, highlighting the need for multidimensional evaluation in responsible synthetic data generation.

Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field at the intersection of quantum computing, natural language understanding, and formal linguistic theory. As advances in quantum hardware and algorithms accelerate, QNLP promises new paradigms for representation learning, semantic modeling, and efficient computation. However, existing literature remains fragmented, with no unified synthesis across modeling, encoding, and evaluation dimensions. In this work, we present the first systematic and taxonomy driven survey of QNLP that holistically organizes research spanning three core dimensions: computational models, encoding paradigms, and evaluation frameworks. First, we analyze foundational approaches that map linguistic structures into quantum formalism, including categorical compositional models, variational quantum circuits, and hybrid quantum classical architectures. Second, we introduce a unified taxonomy of encoding strategies, ranging from quantum tokenization and state preparation to embedding based encodings, highlighting tradeoffs in scalability, noise resilience, and expressiveness. Third, we provide the first comparative synthesis of evaluation methodologies, benchmark datasets, and performance metrics, while identifying reproducibility and standardization gaps.We further contrast quantum inspired NLP methods with fully quantum implemented systems, offering insights into resource efficiency, hardware feasibility, and real world applicability. Finally, we outline open challenges such as integration with LLMs and unified benchmark design, and propose a research agenda for advancing QNLP as a scalable and reliable discipline.