Importance Analysis for Dynamic Control of Balancing Parameter in a Simple Knowledge Distillation Setting

Although deep learning models owe their remarkable success to deep and complex architectures, this very complexity typically comes at the expense of real-time performance. To address this issue, a variety of model compression techniques have been proposed, among which knowledge distillation (KD) stands out for its strong empirical performance. The KD contains two concurrent processes: (i) matching the outputs of a large, pre-trained teacher network and a lightweight student network, and (ii) training the student to solve its designated downstream task. The associated loss functions are termed the distillation loss and the downsteam-task loss, respectively. Numerous prior studies report that KD is most effective when the influence of the distillation loss outweighs that of the downstream-task loss. The influence(or importance) is typically regulated by a balancing parameter. This paper provides a mathematical rationale showing that in a simple KD setting when the loss is decreasing, the balancing parameter should be dynamically adjusted

HyperCLOVA X Technical Report

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.