Researchers from Shanghai Jiao Tong University have discovered that large language models can effectively learn complex reasoning tasks without the need for extensive datasets. Their study introduces the concept of “less is more,” demonstrating that with only a few hundred carefully curated examples, these models can achieve impressive results. For instance, a model fine-tuned on 817 training examples reached 57.1 percent accuracy on a challenging benchmark, outperforming models trained on significantly larger datasets. The findings suggest that the inherent knowledge acquired during the pre-training phase allows these models to generalize effectively, making it feasible for enterprises to customize models without the extensive resources typically required.
Why do we care?
This research challenges the prevailing assumption that fine-tuning large language models (LLMs) requires massive datasets. If models can achieve competitive performance with only a few hundred carefully selected examples, this could drastically lower the cost and resource burden for enterprises looking to customize AI solutions. We’re seeing the further commoditization of the models.
Let’s note that data remains the key leverage. Commoditization heavily benefits companies with access to high-quality domain knowledge. Organizations with deep industry expertise will have an edge in selecting the best examples, leaving those without such expertise at a disadvantage.
