OPT-IML vs supervised.co

The paper titled "OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization" focuses on fine-tuning large pre-trained language models with a technique called instruction-tuning, which has been demonstrated to improve model performance on zero and few-shot generalization to unseen tasks. The main challenge addressed in the study is grasping the performance trade-offs due to different decisions made during instruction-tuning, such as task sampling strategies and fine-tuning objectives.

The authors introduce the OPT-IML Bench—a comprehensive benchmark comprising 2000 NLP tasks from 8 different benchmarks—and use it to evaluate the instruction tuning on OPT models of varying sizes. The resulting instruction-tuned models, OPT-IML 30B and 175B, exhibit significant improvements over vanilla OPT and are competitive with specialized models, further inspiring the release of the OPT-IML Bench framework for broader research use.

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