@inproceedings{
li2023chain,
title={Chain of Code: Reasoning with a Language Model-Augmented Code Interpreter},
author={Chengshu Li and Jacky Liang and Fei Xia and Andy Zeng and Sergey Levine and Dorsa Sadigh and Karol Hausman and Xinyun Chen and Li Fei-Fei and brian ichter},
booktitle={NeurIPS 2023 Foundation Models for Decision Making Workshop},
year={2023},
url={https://openreview.net/forum?id=tlRUbI0Yf3}
}
Code provides a general syntactic structure to build complex programs and perform precise computations when paired with a code interpreter – we hypothesize that language models (LMs) can leverage code-writing to improve Chain of Thought reasoning not only for logic and arithmetic tasks, but also for semantic ones (and in particular, those that are a mix of both). For example, consider prompting an LM to write code that counts the number of times it detects sarcasm in an essay: the LM may struggle to write an implementation for "detect_sarcasm(string)" that can be executed by the interpreter (handling the edge cases would be insurmountable). However, LMs may still produce a valid solution if they not only write code, but also selectively "emulate" the interpreter by generating the expected output of "detect_sarcasm(string)" and other lines of code that cannot be executed. In this work, we propose Chain of Code (CoC), a simple yet surprisingly effective extension that improves LM code-driven reasoning. The key idea is to encourage LMs to format semantic sub-tasks in a program as flexible pseudocode that the interpreter can explicitly catch undefined behaviors and hand off to simulate with an LM (as an "LMulator"). Experiments demonstrate that Chain of Code outperforms Chain of Thought and other baselines across a variety of benchmarks; on BIG-Bench Hard, Chain of Code achieves 84%, a gain of 12% over Chain of Thought. CoC scales well with large and small models alike, and broadens the scope of reasoning questions that LMs can correctly answer by "thinking in code". Project website: https://chain-of-code.github.io
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Chengshu Li Jacky Liang Andy Zeng Xinyun Chen Karol Hausman Dorsa Sadigh Sergey Levine Li Fei-Fei Fei Xia brian ichter