Manuel Faysse


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Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
Manuel Faysse | Gautier Viaud | Céline Hudelot | Pierre Colombo
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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.