As large models continue to evolve and become increasingly intelligent, the key to them truly understanding our needs lies in instruction tuning. Experts from Tencent Youtu Lab and Shanghai Jiao Tong University have collaborated to publish an in-depth review on the evaluation and selection of instruction tuning datasets, unveiling the mysteries behind enhancing the performance of large models.
The goal of large models is to master the essence of natural language processing, and instruction tuning is a crucial step in their learning process. The experts have conducted a thorough analysis on how to evaluate and select datasets to ensure that large models perform exceptionally well across various tasks.
This review not only boasts an impressive length but also covers over 400 related literature sources, providing a detailed guide from the dimensions of data quality, diversity, and importance.
Data quality directly impacts the effectiveness of instruction tuning. The experts have proposed various evaluation methods, including manually designed metrics, model-based metrics, GPT automated scoring, and indispensable human evaluations.
Diversity assessment focuses on the richness of the dataset, including vocabulary, semantics, and the overall data distribution diversity. A diverse dataset enables the model to generalize better across various scenarios.
Importance assessment identifies the most critical samples for model training. This not only improves training efficiency but also ensures the model's stability and accuracy when facing complex tasks.
Although current research has achieved certain results, experts also point out challenges such as the weak correlation between data selection and model performance, and the lack of a unified standard to evaluate instruction quality.
Looking ahead, experts call for the establishment of specialized benchmarks to evaluate instruction tuning models and enhance the interpretability of the selection pipeline to adapt to different downstream tasks.
This research by Tencent Youtu Lab and Shanghai Jiao Tong University not only provides us with a valuable resource but also points the way forward for the development of large models. With continuous technological advancements, we have reason to believe that large models will become more intelligent and better serve humanity.