Revisiting Instruction Fine-tuned Model Evaluation To Guide Industrial Applications

Faysse Manuel, Viaud Gautier, Hudelot Céline, Colombo Pierre. 2023

[Paper]    
Applications Fine Tuning Pretraining Methods RAG Reinforcement Learning Training Techniques

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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