Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications

• URL: http://arxiv.org/abs/2310.14103v1
• Date: Sat, 21 Oct 2023 20:04:55 GMT
• Title: Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
• Authors: Manuel Faysse, Gautier Viaud, C\'eline Hudelot, Pierre Colombo
• Abstract summary: Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs) We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies. Our findings offer practitioners actionable insights for real-world IFT model deployment.
• Score: 11.035667183761207
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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