While Chain-of-Thought (CoT)  prompting boosts Language Models’ (LM)  performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka.faithfulness) . We proposeFaithful CoT, a reasoning framework involving two stages:Translation(Natural Language querysymbolic reasoning chain)  andProblem Solving(reasoning chainanswer) , using an LM and a deterministic solver respectively. This guarantees that the reasoning chain provides a faithful explanation of the final answer. Aside from interpretability, Faithful CoT also improves empirical performance: it outperforms standard CoT on 9 of 10 benchmarks from 4 diverse domains, with a relative accuracy gain of 6.3% on Math Word Problems (MWP) , 3.4% on Planning, 5.5% on Multi-hop Question Answering (QA) , and 21.4% on Relational Inference. Furthermore, with GPT-4 and Codex, it sets the new state-of-the-art few-shot performance on 7 datasets (with 95.0+ accuracy on 6 of them) , showing a strong synergy between faithfulness and accuracy.111Our code, data, and prompts are available athttps://github.com/veronica320/Faithful-COT.