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Inside Chain of Code: Google DeepMind Method that can Reason in Code
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Inside Chain of Code: Google DeepMind Method that can Reason in Code

Last Updated on December 30, 2023 by Editorial Team

Author(s): Jesus Rodriguez

Originally published on Towards AI.

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Writing code is a clear expression of reasoning. A typical program includes blocks that express control-flow, logical expressions and modular structures that combine to model a solution to a given problem. Could code be used to improve LLM reasoning? Google DeepMind recently published a paper proposing Chain-of-code(CoC), a reasoning method for foundation model based on code generation.

The inspiration for CoC comes from some of the limitations of chain-of-thought(CoT) prompting when it comes to dealing with arithmetic tasks. CoC tries to mitigate that by creating models that can “reason in code form”. CoC operates by allowing the LLM to not only write code but also to mimic the behavior of an interpreter. This is done by generating anticipated outputs for specific code segments that an actual interpreter might struggle to execute. The central concept behind CoC is to enable LLMs to break down complex semantic tasks within a program into manageable pseudocode segments. These segments are then processed in real-time, a process referred to as LMulator, a blend of the terms LM and emulator.

For instance, consider a task where the LM is asked to determine the frequency of sarcasm in a given paragraph. CoC enables the LM to construct a program that might utilize functions like is_sarcastic(sentence). Here, the LM predicts and returns a boolean result based on linguistic analysis, which is then integrated into the broader program structure. The process CoC follows is straightforward: the LM writes the code, and as the interpreter attempts to execute each line (marked in red), it either succeeds or, in case of failure, the LM steps in to simulate the result (indicated in purple) and updates the program state (shown in green). This dual approach of CoC, where it combines writing executable code for precise algorithmic computations and creating pseudocode for semantic challenges, not only simplifies the programming process but also enables LMs to process and ‘think’ in code more effectively. The following figure illustrates that concept:

Image Credit: Google DeepMind

The Architecture

Chain of Code(CoC) is based on three pivotal methods in LLM reasoning: Chain of Thought (CoT), ScratchPad, and Program of Thoughts(PoT). These approaches have significantly enhanced the capability of language models to decompose complex problems into manageable substeps. The CoT approach utilizes natural language to break down problems, reflecting how one might methodically work through a challenging issue. ScratchPad, in contrast, keeps track of intermediate steps like a code interpreter, aiding in the simulation of code output. The third approach, PoT, concentrates on the creation of code that is executed by a code interpreter for problem-solving.

Image Credit: Google DeepMind

DeepMind’s CoC methodology is inspired by human problem-solving techniques, where a blend of natural language, pseudocode, and code execution is employed. This method consists of two phases: Generation and Execution. During Generation, in response to a given problem, the language model formulates code for reasoning. In the Execution phase, this code is either executed through a code interpreter or simulated by a language model if direct execution isn’t feasible.

Image Credit: Google DeepMind

CoC Generation involves the creation of code that structures the reasoning process for solving problems. This code can vary from explicit code to pseudocode or natural language. An example of this can be seen in how DeepMind tackled an object counting problem from BIG-Bench.

A critical aspect of CoC is its Execution process. Here, once the reasoning code is developed, it’s first attempted to be run by a code interpreter. If the code runs successfully, the process continues with the updated program state. However, if the code fails to execute or encounters errors, the language model steps in to simulate the execution, updating the program state based on the model’s outputs. This approach, termed as an “LMulator” (a blend of language model and code emulator), opens new avenues for code applications, especially those involving a mix of semantics and numerics. This method is exemplified in how the generated code is executed, maintaining the program state and alternating between a Python executor and the LMulator.

The Results

Google DeepMind evaluated CoC across different benchmarks. CoC seems to regularly outperform other methods in the number of tasks that surpass the human baseline. The following matrix illustrates the performance in few-shot prompting tasks for both single-task and cross-task scenarios.

Image Credit: Google DeepMind

If strong benchmarks such as BIG-Bench-Hard(BBH), CoC was able to perform incredibly well across complex tasks that involve different forms of reasoning:

Image Credit: Google DeepMind

Reasoning remains one of the most important areas to unlock the potential of LLMs. Coding is one of the clearest forms of reasoning, and DeepMind’s CoC is one of the first models that combine these two areas for general-purpose LLM scenarios.

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