Neural language models (LMs) have become popular due to their extensive theoretical work mainly focused on representation ability. A previous study of the representation capability using sequential Boolean models helps to properly understand its lower and upper limit and the potential of the transformer architecture. LMs have become the backbone of many NLP tasks and most of the state-of-the-art LMs are based on a transformative architecture. Furthermore, formal computational models offer a fluid and precise formulation for studying different aspects of probability distributions that LMs can handle.
However, the LM architecture is primarily examined in the context of binary language recognition, which creates a category error between LM (distribution over strings) and theoretical abstraction (a set of strings). To solve this problem, it is important to determine the kinds of probability distributions over strings represented by the transformer. Furthermore, the analysis of the architecture for language acceptance is the main area of interest for most researchers. However, the researchers of this article argue that this is not the optimal approach to solve such a problem in the field of LMs, which are probability distributions over chains.
Researchers from eth Zurich studied the LM representative capacity of transformers with n-gram LMs. They successfully demonstrated that it is easy to capture the parallelizable nature of n-gram LMs with the help of the transformer architecture, providing several lower bounds on the probabilistic representation capability of transformer LMs. These transformer LMs consist of multiple transformer layers and represent n-gram LMs that require intense and sparse attention, showing several ways in which transformer LMs can simulate n-gram LMs. It uses the attention mechanism to improve input representations, including queries, keys, and values, by evaluating their updated versions.
The researchers gave two theorems to explain the representative ability of LMs of hard-attention transformers. The first theorem states that, for any n-gram LM, there exists a weakly equivalent single-layer hard attention transformer LM with n – 1 head. Your proof intuition is that a weakly equivalent LM defined by a transformer looking back to the previous n – 1 positions is constructed using n – 1 heads. The second theorem states that, for any n-gram LM, there exists a weakly equivalent n – 1-layer hard attention transformer LM with a single head. Your proof intuition is that an n – 1 layer LM transformer can use the n – 1 layers to look back at the immediately previous position and copy it forward n – 1 times.
Transformative LMs and traditional LMs are wired to capture any n-gram LMs using the hard and sparse attention transformative LM method, which provides a stable lower bound on their probabilistic representation ability. Furthermore, the role of multiple heads and the number of layers consists of a balance between the number of heads, layers and the complexity of nonlinear transformations required to simulate n-gram LMs. Overall, these results contribute to the probabilistic representation capabilities of transformative LMs and the mechanisms they could use to run formal computational models.
In conclusion, ETHzurich researchers studied the representativeness of transformer LMs with n-gram LMs, capturing the parallelizable nature of n-gram LMs using the transformer architecture and providing multiple lower bounds. The researchers demonstrated that transformative LMs can represent n-gram LMs using both intense and sparse attention, demonstrating several mechanisms they can use to present n-gram LMs. However, some limitations have been highlighted for future work: ngram LMs represent a very simple class of LMs, resulting in loose lower bounds, causing transformer LMs to exhibit a more complex structure than ngrams.
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Sajjad Ansari is a final year student of IIT Kharagpur. As a technology enthusiast, he delves into the practical applications of ai with a focus on understanding the impact of ai technologies and their real-world implications. His goal is to articulate complex ai concepts in a clear and accessible way.
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