This paper explores the behavior of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, through the lens of character-level language models. The authors aim to understand how LSTMs learn and represent long-range dependencies in text, and how they compare to traditional n-gram models. They analyze the performance of LSTMs on two datasets: Leo Tolstoy's "War and Peace" and the Linux Kernel source code. The results show that LSTMs significantly outperform n-gram models in tasks requiring long-range reasoning, such as predicting characters that depend on previous context. The study also reveals that LSTMs can maintain long-term information, such as line lengths, quotes, and brackets, through interpretable cells. The authors further analyze the types of errors made by LSTMs and find that they can be categorized into different types, with some errors being more challenging to correct. They also show that increasing the size of the model reduces errors in the n-gram category, suggesting that architectural improvements may be necessary to address remaining errors. The paper concludes that LSTMs are capable of learning powerful, often interpretable long-range interactions on real-world data, and that further research is needed to fully understand their capabilities and limitations.This paper explores the behavior of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, through the lens of character-level language models. The authors aim to understand how LSTMs learn and represent long-range dependencies in text, and how they compare to traditional n-gram models. They analyze the performance of LSTMs on two datasets: Leo Tolstoy's "War and Peace" and the Linux Kernel source code. The results show that LSTMs significantly outperform n-gram models in tasks requiring long-range reasoning, such as predicting characters that depend on previous context. The study also reveals that LSTMs can maintain long-term information, such as line lengths, quotes, and brackets, through interpretable cells. The authors further analyze the types of errors made by LSTMs and find that they can be categorized into different types, with some errors being more challenging to correct. They also show that increasing the size of the model reduces errors in the n-gram category, suggesting that architectural improvements may be necessary to address remaining errors. The paper concludes that LSTMs are capable of learning powerful, often interpretable long-range interactions on real-world data, and that further research is needed to fully understand their capabilities and limitations.