Stupid question: Can a small LLM be given additional training to enlarge it?

Exploring the Possibility of Expanding Small Language Models with Specialized Training

In the rapidly evolving landscape of artificial intelligence and machine learning, the concept of enhancing the capabilities of existing models often sparks intriguing discussions. One thought-provoking idea is whether a compact language model (LLM) can be further trained or extended to improve its understanding and capabilities, particularly through targeted datasets.

An Analogous Concept from Evolutionary Biology

Reflecting on biological evolution, early organisms possessed rudimentary light-sensitive cells, capable only of distinguishing light from darkness. Over time, these simple systems evolved into sophisticated visual mechanisms. Drawing a parallel, could a small language模型—initially limited in scope—be “grown” or trained with specialized data to develop more complex understanding?

Proposed Method: Training on Visual and Annotated Data

Suppose we have a small LLM that we aim to expand. One approach involves training it on annotated images, such as binocular photographs with detailed metadata. For example, datasets could include annotations like “white dot at coordinates (512, 0), intraocular distance 60mm, computed object distance 20 meters,” and so forth.

By generating and analyzing thousands of such permutations—varying colors, scales, shifts, and distances—the model could learn to associate visual cues with spatial relationships. Progressive steps might include tracking moving dots, objects occlusion, and overlapping features, ultimately enabling the model to develop internal representations of object detection and depth perception.

Potential and Limitations

While the dataset could be enormous, this targeted training might help the model internalize concepts related to spatial awareness and object recognition. Essentially, it could simulate a basic form of visual understanding within a language model.

However, practical implementation poses challenges. Scaling up such datasets, ensuring effective training, and integrating visual data into predominantly text-based models require significant resources and expertise. Moreover, the feasibility of “growing” a language model in this manner remains speculative.

Final Thoughts

This idea delves into the fascinating intersection of language models and visual perception. While currently ambitious and perhaps infeasible with existing technology, exploring these avenues could pave the way for more integrated and multimodal AI systems in the future.