# Successive Prompting for Decomposing Complex Questions

**Authors:** Dheeru Dua, Shivanshu Gupta, Sameer Singh, Matt Gardner  
**arXiv:** [2212.04092](https://arxiv.org/abs/2212.04092)  
**Date:** 2022-12-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2212.04092v1/

## Key points

- Complex question answering with limited supervision is challenging
- Large language models (LMs) can perform complex question answering in a few-shot setting
- Previous approach requires manually written intermediate supervision, which is expensive to collect
- Successive Prompting breaks down complex tasks into simple tasks and solves them iteratively
- Decouples supervision for decomposing complex questions from the supervision for answering simple questions
- Three types of model outputs: intermediate questions qk, intermediate answers ak, and the final answer y
- Model alternates between QD and QA until it terminates
- Successive prompting can be used with model fine-tuning using synthetic data
- Best model achieved an improvement of approximately 5% absolute F1 on a few-shot version of the DROP dataset compared with state-of-the-art models with similar supervision.

## Summary

The ability to answer complex questions that require making latent decisions is a challenging task, particularly when limited supervision is available. Recent works have utilized large language models (LMs) to perform complex question answering in a few-shot setting by outputting intermediate rationalizations while solving the complex question in a single pass. However, this approach requires manually written intermediate supervision, which can be expensive to collect. To address this issue, researchers have introduced "Successive Prompting," where they iteratively break down a complex task into simple tasks and solve them until they arrive at the final solution. This approach decouples the supervision for decomposing complex questions from the supervision for answering simple questions, allowing for multiple opportunities to query in-context examples at each reasoning step and learn question decomposition separately from question answering using synthetic data. There are three types of model outputs: intermediate questions qk, intermediate answers ak, and the final answer y. The first type of output is referred to as question decomposition (QD), while the second type is known as question answering (QA). Final answer prediction is treated as a special case of QD where the model decides that no more decomposition is necessary and outputs a final answer. The model iteratively alternates between QD and QA until it terminates. Successive prompting can also be used with model fine-tuning, where each intermediate output serves as training examples for LMs. The researchers generated a synthetic dataset that can bootstrap a model's ability to decompose and answer intermediate questions. Their best model achieved an improvement of approximately 5% absolute F1 on a few-shot version of the DROP dataset compared with state-of-the-art models with similar supervision. Overall, Successive Prompting provides an effective way to decompose complex questions into simpler ones while learning how to answer them separately using large language models or bespoke components when necessary.