The rapid advancements in language models have been primarily attributed to their massive scale, enabling mind-blowing capabilities in various natural language processing tasks. Nonetheless, a thought-provoking query arises: is scale the one determinant of model performance? A recent study challenges this notion and investigates whether smaller models, despite their reduced size, can compete with the biggest models available today. By leveraging progressive distillation, constrained decoding, and self-imitation learning algorithms, the study introduces a groundbreaking framework called I2D2, which empowers smaller language models to outperform models which might be 100 times larger.

Empowering Smaller Models with I2D2

The first challenge smaller language models face is their relatively lower generation quality. The I2D2 framework overcomes this obstacle through two key innovations. Firstly, it employs neurologic decoding to perform constrained generation, leading to slight improvements in generation quality. Moreover, the framework incorporates a small critic model that filters out low-quality generations, allowing for substantial enhancements in performance. The language model is fine-tuned in the next self-imitation step using its high-quality generations obtained after critic filtering. Importantly, these steps could be iteratively applied to enhance the performance of smaller language models constantly.

Application to Generating Commonsense Knowledge

Within the context of generating commonsense knowledge about on a regular basis concepts, the I2D2 framework demonstrates impressive results. Unlike other approaches that depend on GPT-3 generations for knowledge distillation, I2D2 stands independently. Despite being based on a model that’s 100 times smaller than GPT-3, I2D2 generates a high-quality corpus of generic commonsense knowledge.

Outperforming Larger Models

Comparative evaluation reveals that I2D2 outperforms GPT-3 in accuracy when generating generics. By examining the accuracy of generics present in GenericsKB, GPT-3, and I2D2, it becomes evident that I2D2 achieves higher accuracy levels despite its smaller model size. The framework’s critic model is pivotal in discerning true and false common sense statements, outshining GPT-3.

Enhanced Diversity and Iterative Improvement

Along with improved accuracy, I2D2 demonstrates greater diversity in its generations in comparison with GenericsKB. The generated content is ten times more diverse, which continues to enhance with successive iterations of self-imitation. These findings illustrate the robustness of I2D2 in generating accurate and diverse generic statements, all while utilizing a model that’s 100 times smaller than its competitors.

Implications of the Study

The important thing findings from this study have far-reaching implications for natural language processing. It highlights that smaller and more efficient language models possess significant potential for improvement. By employing novel algorithmic techniques akin to those introduced in I2D2, smaller models can rival the performance of larger models in specific tasks. Moreover, the study challenges the notion that self-improvement is exclusive to large-scale language models, as I2D2 demonstrates the aptitude of smaller models to self-iterate and enhance their generation quality.

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