Different tokens in natural language have varying prediction difficulties. Tokens can either require minimal context or be complex, such as theorem names or exam answers. Language models use residual connections, refining output distributions across layers. An early exit strategy allows for variable computational resources at each token position. Multi-token prediction losses promote information-sharing between adjacent tokens, optimizing computational resources to focus where they provide the greatest benefit. This investigation utilizes a polynomial arithmetic task integrated with pause tokens to test the computation-sharing hypothesis effectively.
The prediction difficulty of different tokens in natural text varies greatly, affecting the training efficacy of language models. Some tokens require minimal context to predict, while others demand significant reasoning.
Multi-token prediction losses encourage information-sharing between adjacent token positions, facilitating more efficient allocation of computational resources in language models to tokens that require it the most.
#language-models #multi-token-prediction #computational-efficiency #token-prediction-difficulty #training-strategies
Collection
[
|
...
]