# Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning

arXiv: https://arxiv.org/abs/2310.13552 · HTML: https://mlsanity.com/paper/2310.13552/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 19 of its 21 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Generate rather than Retrieve: Large Language Models are Strong Context Generators](https://mlsanity.com/paper/2209.10063/) (2022)
- [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://mlsanity.com/paper/2011.03088/) (2020)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [Multi-hop Question Answering](https://mlsanity.com/paper/2204.09140/) (2022)

## Built on by (most cited first, 21 of 21)

- [ESCoT: Towards Interpretable Emotional Support Dialogue Systems](https://mlsanity.com/paper/2406.10960/) (2024)
- [Large Language Models Can Self-Correct with Key Condition Verification](https://mlsanity.com/paper/2405.14092/) (2024)
- [MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge](https://mlsanity.com/paper/2412.17032/) (2024)
- [PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering](https://mlsanity.com/paper/2402.11034/) (2024)
- [SGIC: A Self-Guided Iterative Calibration Framework for RAG](https://mlsanity.com/paper/2506.16172/) (2025)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering](https://mlsanity.com/paper/2407.02964/) (2024)
- [RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning](https://mlsanity.com/paper/2410.03122/) (2024)
- [DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs](https://mlsanity.com/paper/2406.17158/) (2024)
- [QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory](https://mlsanity.com/paper/2408.10497/) (2024)
- [Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning](https://mlsanity.com/paper/2602.22642/) (2026)
- [Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2411.07820/) (2024)
- [AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations](https://mlsanity.com/paper/2311.13538/) (2023)
- [SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine](https://mlsanity.com/paper/2410.17021/) (2024)
- [Reducing Hallucinations: Enhancing VQA for Flood Disaster Damage Assessment with Visual Contexts](https://mlsanity.com/paper/2312.13848/) (2023)
- [Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning](https://mlsanity.com/paper/2401.15043/) (2024)
- [Certifying Knowledge Comprehension in LLMs](https://mlsanity.com/paper/2402.15929/) (2024)
- [From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework](https://mlsanity.com/paper/2605.02939/) (2026)
- [MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis](https://mlsanity.com/paper/2606.21119/) (2026)
- [THOR: A Theta-Gamma Hierarchical Oscillatory Reasoning Framework for Multi-hop QA](https://mlsanity.com/paper/2607.20459/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

In open-domain question-answering (ODQA), most existing questions require single-hop reasoning on commonsense. To further extend this task, we officially introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop questions with explicit reasoning steps in open-domain setting. Recently, large language models (LLMs) have found significant utility in facilitating ODQA without external corpus. Furthermore, chain-of-thought (CoT) prompting boosts the reasoning capability of LLMs to a greater extent with manual or automated paradigms. However, existing automated methods lack of quality assurance, while manual approaches suffer from limited scalability and poor diversity, hindering the capabilities of LLMs. In this paper, we propose Self-prompted Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality CoTs of LLMs, by LLMs and for LLMs. SP-CoT introduces an automated generation pipeline of high quality ODMR datasets, an adaptive sampler for in-context CoT selection and self-prompted inference via in-context learning. Extensive experiments on four multi-hop question-answering benchmarks show that our proposed SP-CoT not only significantly surpasses the previous SOTA methods on large-scale (175B) LLMs, but also nearly doubles the zero-shot performance of small-scale (13B) LLMs. Further analysis reveals the remarkable capability of SP-CoT to elicit direct and concise intermediate reasoning steps by recalling ~50% of intermediate answers on MuSiQue-Ans dataset.