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How LSTM Processes Text

Visualize how Long Short-Term Memory networks pass both a Cell State (long-term memory) and a Hidden State (short-term memory) forward to process sequences.

LSTM Analysis

Time Steps -
State Size -
Shared Parameters -
Cell State ($C_t$) - Long Term
Hidden State ($H_t$) - Short Term

LSTMs and Text: Mastering Long-Term Memory

Discover how Long Short-Term Memory networks use a dual-state architecture to overcome the limitations of standard RNNs.

The Vanishing Gradient Problem

Standard Recurrent Neural Networks (RNNs) pass a single "hidden state" forward through time. While great in theory, standard RNNs suffer from the vanishing gradient problem. During training, as the network looks back across many time steps (long sentences), the signals necessary to update the weights become microscopically small.

The result? A standard RNN essentially develops "amnesia." By the time it reads the 20th word in a sentence, it has mostly forgotten the 1st word. This makes it terrible at understanding long-range dependencies in text.

The Dual State Solution: Cell and Hidden States

LSTMs solve this amnesia by introducing a more complex internal structure that passes two distinct states forward at every time step (as seen in the visualization):

  1. The Cell State ($C_t$): Often compared to a conveyor belt. It runs straight down the entire chain, with only minor linear interactions. It's very easy for information to flow along it unchanged. This is the LSTM's long-term memory.
  2. The Hidden State ($H_t$): This is the LSTM's short-term memory or its "focus" for the current time step. It is derived from the Cell State and is what gets passed up as the output ($Y_t$) for that specific step.

The Four Gates (Why so many parameters?)

To control what gets added to or removed from the Cell State conveyor belt, the LSTM uses neural network layers called gates. Inside every single LSTM cell block shown in the visual, there are actually four separate fully-connected layers operating simultaneously:

  • Forget Gate ($f_t$): Decides what information to throw away from the past cell state.
  • Input Gate ($i_t$): Decides which new values to update in the cell state.
  • Cell Candidate ($\tilde{C}_t$): Creates a vector of new candidate values that could be added to the state.
  • Output Gate ($o_t$): Decides what parts of the cell state to output as the new Hidden State ($H_t$).

Because there are 4 independent layers doing calculations on the inputs ($X_t$) and previous hidden state ($H_{t-1}$), an LSTM has roughly 4 times as many parameters as a standard RNN of the same size. Click the Parameters button to see the math.

Interactive Exploration

  • Run the Simulation: Watch the blue data ($X_t$) enter the cell, merge with the past states, and then split into the Cyan conveyor belt ($C_t$) and the Green focus state ($H_t$).
  • Check Many-to-One vs Many-to-Many: In Many-to-One (like Sentiment Analysis), only the final $H_t$ is pushed up to the classifier to make the final $Y$ decision. In Many-to-Many (like Language Translation or Text Generation), an output is generated at every step.
Cheat sheet

How LSTM Processes Text

Visualize how Long Short-Term Memory networks pass both a Cell State (long-term memory) and a Hidden State (short-term memory) forward to process sequences.

NLP · vizlearn.in/natural_language_processing/how_lstm_processes_text.html

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