Challenge: Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources.
Approach: They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question.
Outcome: The proposed framework outperforms SOTA methods on complex QA datasets.

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Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)

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Challenge: Existing methods to answer complex questions rely on decomposition of complex questions into sub-questions . Existing approaches to decompose complex questions are limited by the original question .
Approach: They propose a question decomposition approach to decompose semantically clear questions . they use the decomposed sub-questions to select relevant patterns as auxiliary information .
Outcome: The proposed method achieves state-of-the-art performance on multiple datasets.
Complex Question Decomposition for Semantic Parsing (P19-1)

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Challenge: Existing methods that ignore the decompositionality of complex questions are not suitable for complex question semantic parsing.
Approach: They propose a hierarchical semantic parsing method which utilizes the decompositionality of complex questions for semantic paring.
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Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data (2025.findings-acl)

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Challenge: a novel method for question answering over mixed sources, like text and tables, has been developed for question-answering . personal information is a prominent case of such heterogeneous data, such as calendar entries, workout statistics, shopping records, streaming history, and more.
Approach: They propose a method that creates an executable operator tree for a given question . they use recursive decomposition to decompose a question into an operator tree .
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Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)

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Challenge: Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions.
Approach: They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result.
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A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)

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Challenge: Existing approaches for Knowledge Base Question Answering focus on a specific knowledge base or evaluating it on underlying knowledge base requires non-trivial changes.
Approach: They propose a framework that separates semantic parsing from knowledge base interaction . they propose KBQA framework that allows generalization across knowledge bases .
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KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)

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Challenge: Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance .
Approach: They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation .
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Unsupervised Question Decomposition for Question Answering (2020.emnlp-main)

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Challenge: Existing QA systems struggle to answer complex questions because information is scattered in different places.
Approach: They propose an unsupervised algorithm that decomposes hard questions into simpler sub-questions . they propose an algorithm that can be used to generate a final answer from millions of questions .
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Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)

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Challenge: Existing KBQA methods focus on simpler questions and do not work well on complex questions . a knowledge-based question answering approach is able to answer complex questions using a standard knowledge base .
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CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)

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Challenge: Existing knowledge base question answering methods struggle with complex queries.
Approach: They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation.
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When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables (2026.acl-long)

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Challenge: Extensive research shows that noisy data significantly degrades the performance of table reasoning in real-world applications.
Approach: They propose a dual denoising framework for complex questions and large-scale tables that uses Tree-guided table pruning to remove irrelevant data step by step.
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