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.
Outcome: The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ.

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ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)

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Challenge: Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors.
Approach: They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly.
Outcome: Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ.
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 .
Outcome: The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks.
RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)

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Challenge: Existing methods for Knowledge Base Question Answering (KBQA) face hallucination problems, resulting in low accuracy.
Approach: They propose a retrieval-generate-retrieve framework that uses a Retrieve-Generate framework to retrieve factual knowledge from a knowledge graph.
Outcome: Experimental results show that RGR-KBQA improves on CWQ and WebQSP datasets.
GRV-KBQA: A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation (2025.findings-emnlp)

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Challenge: Existing methods for Knowledge Base Question Answering generate non-executable queries and inefficiencies in query execution.
Approach: a framework that decouples logical structure generation from semantic grounding is proposed . the framework explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures.
Outcome: GRV-KBQA decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy.
Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (2022.coling-1)

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Challenge: Existing generation-based KBQA methods that translate natural language questions to executable logical forms are proving promising but noise introduced can lead to incorrect results.
Approach: They propose a Generation-based KBQA method that uses auxiliary information to enhance logical form generation by combining unseen KB items with novel combinations.
Outcome: The proposed method achieves state-of-the-art results on ComplexWebQuestions and WebQuestIONSSP datasets.
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models (2024.acl-long)

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Challenge: Knowledge base question answering (KBQA) is a challenging task, particularly in parsing intricate questions into executable logical forms.
Approach: They propose a framework to generate logical forms through direct interaction with knowledge bases (KBs) by annotating a dataset with step-wise reasoning processes.
Outcome: The proposed framework achieves competitive results on the WebQuestionsSP, ComplexWebQuestIONS, KQA Pro, and MetaQA datasets with a minimal number of examples (shots). Importantly, the proposed model supports manual intervention, allowing for the iterative refinement of LLM outputs.
Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation (2025.emnlp-main)

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Challenge: Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs.
Approach: They propose a model that injects schema contexts into entity retrieval and logical form generation to enhance generalizability.
Outcome: The proposed model outperforms state-of-the-art models on two commonly used benchmark datasets across a variety of test settings.
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.
RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions (2024.acl-long)

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Challenge: Existing knowledge base question answering models assume all questions to be answerable.
Approach: They propose a new KBQA model that unifies two key ideas in a single architecture . they propose logical form discrimination and sketch-filling-based construction for unanswerable questions .
Outcome: The proposed model outperforms existing models in handling answerable and unanswerable questions.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)

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Challenge: Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale .
Approach: They propose a compositional programming language to represent the reasoning process of complex questions.
Outcome: The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model .

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