Chain of Code (CoC): Enhancing Language Model Reasoning
A team of researchers from Google DeepMind, Stanford University, and the University of California, Berkeley has developed a new approach to improve the code-driven reasoning of language models. This new method, known as Chain of Code(CoC), allows language models to handle semantic sub-tasks through flexible pseudocode, addressing undefined behaviors and offloading the simulation of these sub-tasks to language models.
Enhancing Reasoning with CoC
Other approaches like Chain of Thought, least-to-most, and ScratchPad have leveraged prompts to improve reasoning by breaking tasks down into steps or maintaining a trace of results. However, LMs trained on Github have shown promising results in solving complex tasks using coded prompts.
The CoC approach emphasizes generating reasoning substeps in the code structure. Furthermore, it allows for the creation of code in various forms, including explicit code, pseudocode, or natural language, making it more flexible in reasoning through complex problems.
Execution of Code and Performance
One of the significant contributions of CoC is its method of execution. After the code is written, it is either executed using a code interpreter or simulated by a language model if it cannot be executed. This results in an improved overall performance of CoC, outperforming other methods and achieving state-of-the-art performance in various studies.
In conclusion, CoC is a promising advance towards enhancing language models’ reasoning through writing code and executing code either with an interpreter or a language model. This approach can leverage both the expressive structure of coding and the powerful tools offered by language models.
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