aiqbench analysis
The paper proposes LLMR, a knowledge distillation method that uses a reward function derived from a large language model to train smaller models for dialogue generation and summarization. Its claim is that this beats traditional distillation baselines, but the abstract gives no quantitative results, model sizes, or baseline details, so the size of the gain is unverified. For practitioners, the question is whether LLM-derived rewards can make compact models viable in resource-constrained settings, and the full experiments will need checking.
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward
arXiv:2409.12500v2 Announce Type: replace-cross 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.
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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2409.12500 [cs.CL]
(or arXiv:2409.12500v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2409.12500 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 181 words
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