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arXiv · 2310.14103

Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications

Manuel Faysse·Gautier Viaud·Céline Hudelot·Pierre Colombo
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Abstract

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 trade-offs that emerge in practical industrial settings. Our findings offer practitioners actionable insights for real-world IFT model deployment.

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Manuel Faysse, Gautier Viaud, Céline Hudelot, Pierre Colombo. 2023-10-21. Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications. https://doi.org/10.18653/v1%2F2023.emnlp-main.559

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