Adapting large pre-trained language models to specific tasks requires balancing performance improvement with preservation of learned capabilities. Standard fine-tuning approaches optimize a single objective function through gradient descent, often leading to catastrophic forgetting [16] or instability in learned representations. Parameter-efficient methods like LoRA [11] constrain modifications to low-dimensional subspaces but limit adaptation scope. We propose Adjacent Possible Exploration (APE), a selective fine-tuning approach that explores multiple parameter modification directions while implementing acceptance criteria to maintain model stability. The method draws conceptual inspiration from evolutionary optimization principles, particularly the biological constraint that viable changes must preserve essential system properties while enabling incremental improvement. APE operates by generating multiple candidate parameter updates through fine-tuning on randomly sampled data subsets, then selecting only those updates that exceed a performance improvement threshold. This creates a filtered optimization process that systematically explores beneficial parameter modifications while rejecting changes that fall within noise levels or potentially destabilize learned representations. Our key contributions include: (1) A practical algorithm for selective fine-tuning that balances exploration and stability, (2) Empirical validation showing superior performance compared to standard adaptation methods, and (3) Analysis of why selective acceptance of parameter modifications leads to more robust model adaptation. 1 The approach demonstrates that systematic exploration of parameter space through filtered selection can achieve better adaptation results than unconstrained optimization, providing a principled framework for controlled model modification that maintains stability while enabling significant performance improvements.

Large models such as Large Language Models (LLMs) and Vision Language Models (VLMs) have transformed artificial intelligence, powering applications in natural language processing, computer vision, and multimodal learning. However, fully fine-tuning these models remains expensive, requiring extensive computational resources, memory, and task-specific data. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a promising solution that allows adapting large models to downstream tasks by updating only a small portion of parameters. This survey presents a comprehensive overview of PEFT techniques, focusing on their motivations, design principles, and effectiveness. We begin by analyzing the resource and accessibility challenges posed by traditional fine-tuning and highlight key issues, such as overfitting, catastrophic forgetting, and parameter inefficiency. We then introduce a structured taxonomy of PEFT methods -- grouped into additive, selective, reparameterized, hybrid, and unified frameworks -- and systematically compare their mechanisms and trade-offs. Beyond taxonomy, we explore the impact of PEFT across diverse domains, including language, vision, and generative modeling, showing how these techniques offer strong performance with lower resource costs. We also discuss important open challenges in scalability, interpretability, and robustness, and suggest future directions such as federated learning, domain adaptation, and theoretical grounding. Our goal is to provide a unified understanding of PEFT and its growing role in enabling practical, efficient, and sustainable use of large models.

Large Language Models (LLMs) have demonstrated their efficacy across a broad spectrum of tasks in healthcare applications. However, often LLMs need to be fine-tuned on taskspecific expert-annotated data to achieve optimal performance, which can be expensive and time consuming. In this study, we fine-tune PaLM-2 (Anil et al. (2023)) with parameter efficient finetuning (PEFT) using noisy labels obtained from gemini-pro 1.0 (Google (2024)) for the detection of Schedule-of-Event (SoE) tables, which specify care plan in clinical trial protocols. We introduce a filtering mechanism to select high-confidence labels for this table classification task, thereby reducing the noise in the auto-generated labels. We show that fine-tuned PaLM-2 with those labels achieves performance that exceeds the gemini-pro 1.0 and other LLMs. Furthermore, its performance is close to a PaLM-2 fine-tuned on labels obtained from non-expert annotators. Our results show that leveraging LLM-generated labels through powerful models like gemini-pro can potentially serve as a viable strategy for improving LLM performance through fine-tuning in specialized tasks, particularly in domains where expert annotations are scarce, expensive, or time-consuming to obtain.