LLMR: Knowledge Distillation with a Large Language Model-Induced Reward

• URL: http://arxiv.org/abs/2409.12500v1
• Date: Thu, 19 Sep 2024 06:27:58 GMT
• Title: LLMR: Knowledge Distillation with a Large Language Model-Induced Reward
• Authors: Dongheng Li, Yongchang Hao, Lili Mou,
• Abstract summary: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks. However, these models are typically computationally expensive and difficult to be deployed in resource-constrained environments. We propose LLMR, a novel knowledge distillation (KD) method based on a reward function induced from large language models.
• Score: 24.455147056857356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks. However, these models are typically computationally expensive and difficult to be deployed in resource-constrained environments. In this paper, we propose LLMR, a novel knowledge distillation (KD) method based on a reward function induced from large language models. We conducted experiments on multiple datasets in the dialogue generation and summarization tasks. Empirical results demonstrate that our LLMR approach consistently outperforms traditional KD methods in different tasks and datasets.

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