Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requirements. We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies, quantifying the tradeoffs that emerge in practical industrial settings. Our findings offer practitioners actionable insights for real-world IFT model deployment.
CITATION STYLE
Faysse, M., Viaud, G., Hudelot, C., & Colombo, P. (2023). Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications. In EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 9033–9048). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.emnlp-main.559
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