Streamlining LLM s: Adaptive Knowledge Distillation for Tailored Language Models (2025)

Abstract: Large language models (LLMs) like GPT-4 and LLaMA-3 offer transformative potential across industries, e.g., enhancing customer service, revolutionizing medical diagnostics, or identifying crises in news articles. However, deploying LLMs faces challenges such as limited training data, high computational costs, and issues with transparency and explainability. Our research focuses on distilling compact, parameter-efficient tailored language models (TLMs) from LLMs for domain-specific tasks with comparable performance. Current approaches like knowledge distillation, fine-tuning, and model parallelism address computational efficiency but lack hybrid strategies to balance efficiency, adaptability, and accuracy. We present ANON - an adaptive knowledge distillation framework integrating knowledge distillation with adapters to generate computationally efficient TLMs without relying on labeled datasets. ANON uses cross-entropy loss to transfer knowledge from the teacher’s outputs and internal representations while employing adaptive prompt engineering and a progressive distillation strategy for phased knowledge transfer. We evaluated ANON’s performance in the crisis domain, where accuracy is critical and labeled data is scarce. Experiments showed that ANON outperforms recent approaches of knowledge distillation, both in terms of the resulting TLM performance and in reducing the computational costs for training and maintaining accuracy compared to LLMs for domain-specific applications.

## introduction in recent years, large language models (llms) have revolutionized the way we interact with technology, setting a dominant trend in the current era of artificial intelligence. industries are transforming themselves by including llms applications ranging from medical diagnostics leveraging interpretable llm-based solutions @xcite , to financial risk analysis and market modeling @xcite , and real-time crisis detection by analyzing text data from news articles and social media @xcite . despite their impressive capabilities, the deployment of llms for domain-specific tasks faces significant challenges. full fine-tuning of these models requires vast labeled datasets and computational resources, discouraging many organizations, particularly those with constrained budgets. therefore, effective strategies for model compression are critical to enable broader, practical use of llms in resource-constrained environments. existing research to address model compression and adaptation include knowledge distillation (kd) @xcite , parameterefficient fine-tuning (peft) @xcite , and model pruning @xcite . they essentially streamline a large model into a more efficient version without significant loss of performance. kd transfers knowledge from a larger "teacher" model to a smaller "student" model, preserving performance while reducing computational overhead @xcite @xcite . peft approaches, such as adapters @xcite , bitfit @xcite , and lora @xcite , optimize a subset of parameters, allowing task-specific adaptation with minimal resource usage. similarly, prompt-based tuning techniques, including prefix and prompt tuning, inject domain-specific information into model inputs without modifying the core architecture. however, these methods often operate in isolation, lacking hybrid mechanisms that integrate their strengths to address the trade-offs between memory efficiency, computational cost, task-specific performance and data limitation. recent work, such as adapter distillation @xcite and language universal adapters @xcite , highlights the potential of combining techniques but leaves room for further exploration of hybrid approaches optimized for domain-specific applications. to address these limitations, we propose anon, a novel framework that combines kd with adapterbased peft for computationally efficient distillation of llms into domain-specific task language models (tlms). anon transfers knowledge using cross-entropy loss, using the teacher's output distribution and internal representations to retain both high-level abstractions and domain-specific details. the framework employs adaptive prompt engineering to optimize distillation, using datadriven prompts to effectively align teacher and student models effectively @xcite . additionally, anon incorporates a progressive distillation strategy, transferring knowledge in stages from simpler to more complex tasks for comprehensive learning. lightweight adapter modules, trained independently while freezing the rest of the model, significantly reduce computational costs, making anon an efficient and scalable solution for domain-specific applications. we evaluate anon on a crisis-signaling task, focusing on early detection of potential crises using a corpus of 219,292 news articles. following the experimental design outlined in @xcite , we assess anon's performance using teacher-student pairs from llama-2 @xcite , opt @xcite , and gpt-2 @xcite . these evaluations benchmark anon against baseline kd methods. the results demonstrate that anon achieves superior performance with significantly lower resource requirements. for instance, the student model llama-2 7b anon , distilled from the llama-2 13b teacher surpasses the teacher's performance while reducing resource consumption by up to 95.24%. these findings highlight anon's capacity to balance computational efficiency and domain-specific task performance, offering a scalable solution for resourceconstrained ai applications. ## adaptive knowledge distillation for domain-specific tlms we propose anon, an adaptive knowledge distillation framework designed to efficiently distill llms into domain-specific task language models (tlms) as shown in fig :1. anon integrates lightweight adapter layers into the student model, enabling efficient training by focusing the distillation process on these new parameters while freezing the rest of the architecture. the framework employs crossentropy loss to align the student model's predictions with the teacher's output distribution, facilitating accurate transfer of knowledge. by leveraging adapters such as lora, qlora, and series adapters @xcite , anon further optimizes training efficiency and reduces computational costs without compromising model performance. the framework also leverages a progressive distillation strategy, where knowledge transfer is conducted in stages, starting with simpler tasks and gradually progressing to more complex ones. this hybrid approach produces a computationally efficient student model, student an on , that achieves performance comparable to its teacher while significantly reducing resource requirements. the resultant model is well-suited for domain-specific applications such as medical diagnostics, risk management, and customer support, providing scalable and deployable solutions for real-world tasks. ## implementation and evaluation based on the proposed framework (cf. figure1), we implemented anon for crisis signaling task following the experimental design outlined in @xcite . in the end, the distilled student an on provides domainspecific crisis signals and delivers alerts with confidence and severity levels. ## results we evaluated anon on the @xcite benchmark, using accuracy, f1, sensitivity, and specificity ( in some cases, anon outperformed standard kd and surpassed the teacher model. notably, llama-2 7b anon achieved 74.22% accuracy, exceeding both its teacher (71.19%) and kd-based student (74.06%), demonstrating enhanced generalization @xcite . despite a 10x parameter reduction in opt models and a 91.7% reduction in gpt-2, anon preserved competitive performance even against the traditional kd method despite being far more efficient. sensitivity generally exceeded specificity due to dataset imbalance, highlighting the need for bias mitigation strategies. we also verified the performance of anon for resource consumption. our finding, detailed in ## conclusion in this work, we present anon, adaptive knowledge distillation for tailored language models (tlms). anon addresses the challenges of limited training data and significant computational constraints associated with training and deploying llms for specific use cases. anon leverages adapters and knowledge-distilling approach to achieve high performance and parameter efficiency in domain-specific applications. it can manage the complexities of dealing with a large corpus of data, supporting multilingual data processing without the burdensome costs associated with fine-tuning llms for downstream tasks. additionally, it also addresses the issues of transparency, explainability, and maintaining accuracy in the complex high-parameter count model. to evaluate our approach we experimented with three different language model families for teacher-model distilling using a qlora adapter for crisis signaling task. the results showcased anon's capability in terms of accuracy and resource consumption for practical scenarios of crisis signaling tasks. it achieved comparable and even exceeded the performance of teacher models, while significantly lowering memory usage by up to 95.24% and reducing parameters by 99.43% for some cases. our framework not only advances the application of llms in crisis management but also lays a solid foundation for future research across various domains. warning', 'caution and advice', and 'safe and harmless'; input: energy sector warns of impending shortages and surging bills in upcoming months; output: 'risk and warning'". to enhance outcomes, incorporating a few manually curated input examples for few-shot prompting with domain-specific samples is recommended. this approach underscores the pivotal importance of precise and thorough prompt design in facilitating effective training and knowledge distillation.