'Think-and-Execute': A machine learning framework that encapsulates the common logical structure of a job using pseudocode for efficient reasoning in large language models (LLMs)

In large language models (LLM), reasoning involves dissecting the logical structure of a problem and converting it into a sequence of logical steps that lead to a solution. For LLMs, this procedure has proven difficult, particularly in algorithmic reasoning where intricate logical patterns must be interpreted and transformed into a series of processes.

Understanding the patterns within a problem and breaking them down into a series of logical stages to arrive at a solution are key components of algorithmic thinking. Although a variety of reasoning tasks have demonstrated the potential of LLMs, algorithmic reasoning remains difficult due to its complex structure.

To convey the reasoning necessary to solve a particular case or issue, recent studies have attempted to address this challenge using programming languages ​​such as Python. It is challenging to write executable code that faithfully captures the reasoning in a single inference call and does so in real time. Even if two instances need the same logic, code created for one cannot be used for another.

In recent research, a team of researchers from Yonsei University and KAIST ai introduced THINK-AND-EXECUTE, a unique architecture that divides the language model's reasoning process into two parts to overcome limitations. The two parts are as follows.

1. THINK: The framework looks for task-level logic in this phase that is shared by all instances of a given task. Pseudocode, which offers a more adaptable and flexible representation than programming languages ​​such as Python, has then been used to express the shared logic.

1. RUN: The framework adapts task-level logic to each unique instance after it has been defined and declared in pseudocode. Subsequently, it emulates the execution of the pseudocode for each occurrence, efficiently using the logic found to solve the problem.

The effectiveness of THINK AND EXECUTE has been demonstrated through extensive testing on seven algorithmic thinking tasks. The framework outperforms multiple robust baselines, including Program of Thought (PoT) and Chain of Thought (CoT), which are based on instance-specific reasoning techniques. This implies that learning task-level logic can help LLMs become more competent reasoners. Although these models have been trained to follow instructions in normal language, results have shown that pseudocode is a more useful tool for directing LLM thinking than natural language.

The team has summarized its main contributions as follows.

1. A new and unique thinking paradigm known as THINK AND EXECUTE has been suggested. This framework encapsulates the common logical structure of a given job using pseudocode. The method allows for more efficient reasoning in LLMs through the use of pseudocode, which provides flexibility and adaptability.

1. The team has shown that THINK AND EXECUTE outperforms well-established baselines such as chain of thought and program thinking, based on substantial research on a variety of algorithmic tasks within the Big-Bench Hard dataset. This demonstrates how well the system works to improve reasoning skills in a variety of subject domains.

1. Using THINK-AND-EXECUTE, the team has demonstrated the effectiveness of the method by effectively transferring the pseudocode produced by an LLM to smaller language models. This indicates that the approach is generalizable and scalable, meaning it can be applied to a variety of model topologies and sizes.

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