Large Language Models and Reasoning

A method for prompting large language models (LLMs) to generate step-by-step solutions, providing reasoning for reaching the final answer.

A new approach that allows LLMs to reason in a continuous latent space, breaking free from the constraint of word-based reasoning.

The process of transforming text into a numerical representation that can be understood by a machine learning model.

The output of the final layer of a Transformer model, representing the model's understanding of the input.

A type of neural network architecture well-suited for processing sequential data like text.

A method for training an LLM using the chain-of-thought approach, but using a continuous latent space rather than words.

Input tokens are fed into an LLM, which generates a sequence of tokens representing the model's reasoning process, leading to the final answer.

The space where the model's internal representations are stored, often represented as continuous vectors.

The Coconut method, which uses continuous latent space reasoning, allows LLMs to explore multiple possible branches before committing to a specific path, similar to Breadth-First Search (BFS). This contrasts with chain-of-thought reasoning which chooses a direction from the start.

The Coconut method significantly enhances LLMs reasoning abilities. By using continuous thought vectors instead of discrete words, it allows the model to reason in a more flexible and nuanced way.

Continuous latent space reasoning in the Coconut method allows LLMs to develop an interesting BFS-like pattern, improving their performance on planning-intensive tasks. This is because the model can explore multiple possibilities before committing to a particular path.

Instead of starting with a standard pretrained model, one direction for future research in LLMs is to directly pretrain them with continuous thoughts. This could enable these models to reason more effectively from the beginning.

Combining latent thoughts (from Coconut) with the standard chain-of-thought might enable LLMs to gain the benefits of both approaches, resulting in a more powerful reasoning system.

Represents the current reasoning state of the Coconut method, derived from the last hidden state of the model.

A special token used to signal the beginning of a latent thought mode in the Coconut method.

A special token used to signal the end of a latent thought mode and the start of language mode in the Coconut method.

The training process for the Coconut method, where the model learns to reason in a continuous latent space by progressively removing reasoning steps and adding thought tokens.

A hyperparameter in the Coconut method that controls the number of thought tokens added in each training stage.

A technique used to determine the switch from latent thought mode to language mode in the Coconut method, where the model predicts the end of internal reasoning based on a classifier.

A technique for switching from latent thought mode to language mode in the Coconut method, where a fixed number of latent thoughts are processed before generating the final answer.

A baseline model that tries to directly generate the answer without any reasoning traces, used for comparison with the Coconut method.

A baseline method that generates reasoning traces using language tokens, used for comparison with the Coconut method.

A baseline method that internalizes reasoning within the model using a different approach compared to the Coconut method.

A multi-stage training approach in the Coconut method, where the model learns by gradually increasing the complexity of the reasoning task at each stage.

The ability of Coconut to handle tasks that require planning and reasoning over multiple steps, such as those involving complex relationships.

A dataset used to evaluate the Coconut method, which requires strong planning ability.

A case study from the ProsQA dataset where the question requires understanding relationships and deducing a connection through multiple steps of reasoning.