Researchers from Google DeepMind, Stanford University, and University of California, Berkeley have developed Chain of Code (CoC) to enhance code-driven reasoning of language models (LMs). CoC leverages pseudocode to improve reasoning and simulation capabilities, achieving state-of-the-art performance and broader scope of problem-solving. The approach combines advantages of code and LM’s knowledge. [50 words]
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Chain of Code (CoC): Improving Language Model (LM) Code-Driven Reasoning
Practical AI Solutions for Middle Managers
Researchers from Google DeepMind, Stanford University, and the University of California, Berkeley have developed Chain of Code (CoC) to enhance the code-driven reasoning of language models. CoC encourages LMs to format semantic sub-tasks in a program as flexible pseudocode, enabling the interpreter to catch undefined behaviors and simulate with an “LMulator.” This approach broadens the scope of reasoning questions LMs can correctly answer by thinking in code.
CoC leverages prompting to improve reasoning by breaking tasks into intermediate steps or maintaining a trace of intermediate results. LMs trained on Github have been prompted to write and execute code, aiding in solving complex questions involving numeric or symbolic reasoning.
To solve a given problem, CoC generates reasoning substeps in the code structure, enabling the use of code in new regimes by combining the advantages of code with the powerful semantic and commonsense knowledge of LMs.
A core contribution of CoC is not just the generation of reasoning code but how it is executed. Once the code is written, it is attempted to be run by a code interpreter. If the code is not executable, the language model is used to simulate the execution.
The overall performance of the CoC approach outperforms other methods, achieving state-of-the-art performance in several studies. It shows improvements in performance as the model size increases and can apply to problems nominally outside the scope of code (e.g., semantic reasoning problems).
If you want to evolve your company with AI, consider leveraging CoC to improve LM code-driven reasoning and redefine your way of work. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually to stay competitive and stay tuned for continuous insights into leveraging AI.
For AI KPI management advice and practical AI solutions, connect with us at hello@itinai.com. Explore the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Google DeepMind Researchers Propose Chain of Code (CoC): A Simple Yet Surprisingly Effective Extension that Improves Language Model (LM) Code-Driven Reasoning
- MarkTechPost
- Twitter – @itinaicom
