KBQA

Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction (2022.findings-acl)

Challenge:	Existing work on question answering over knowledge bases limited the search space to a subset of KBs . a retrieval-and-rerank framework is used to access KB and rerank retrieved candidates with more powerful neural networks.
Approach:	They propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer.
Outcome:	The proposed method improves accuracy and accuracy on the SimpleQuestions dataset and the FreebaseQA dataset.

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Lingbo Mo, Ashley Lewis, Huan Sun, Michael White

Knowledge Base Question Answering through Recursive Hypergraphs (2021.eacl-main)

Challenge:	Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) but natural language may contain ambiguity and variability, making this challenge difficult.
Approach:	They propose an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps.
Outcome:	The proposed framework improves parsing accuracy and transparency in a crowdsourced dialogue dataset.

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Naganand Yadati, Dayanidhi R S, Vaishnavi S, Indira K M, Srinidhi G

ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021.acl-demo)

Challenge:	Existing methods for Knowledge Base Question Answering (KBQA) do not explicitly incorporate the recursive relational group structure in the given knowledge base.
Approach:	They propose a method to model KBs through recursive hypergraphs using hypergraph data.
Outcome:	The proposed method is based on recursive hypergraphs and has been released on multiple benchmarks.

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Shuang Chen, Qian Liu, Zhiwei Yu, Chin-Yew Lin, Jian-Guang Lou, Feng Jiang

How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)

Challenge:	Existing neural semantic parsing methods for knowledge base question answering are lacking . a generic and extensible framework is lacking for KBQA.
Approach:	They propose a neural semantic parsing framework for large scale knowledge base question answering . they propose 'retriever-transducer-checker' framework that provides a retriever and a transducer .
Outcome:	The proposed framework is ranked at top1 overall performance on the GrailQA leaderboard and achieves competitive performance on typical WebQuestionsSP benchmark.

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Jinxin Liu, Shulin Cao, Jiaxin Shi, Tingjian Zhang, Lunyiu Nie, Linmei Hu, Lei Hou, Juanzi Li

FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)

Challenge:	Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing.
Approach:	They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs.
Outcome:	The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge.

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Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang, Cuiping Li, Hong Chen, Juanzi Li

Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)

Challenge:	Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression.
Approach:	They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression.
Outcome:	The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline.

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Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang

Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge (2023.findings-emnlp)

Challenge:	Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored.
Approach:	They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks.
Outcome:	The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively.

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Shajith Ikbal, Udit Sharma, Hima Karanam, Sumit Neelam, Ronny Luss, Dheeraj Sreedhar, Pavan Kapanipathi, Naweed Khan, Kyle Erwin, Ndivhuwo Makondo, Ibrahim Abdelaziz, Achille Fokoue, Alexander Gray, Maxwell Crouse, Subhajit Chaudhury, Chitra Subramanian

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

Challenge:	Various approaches have been tried to map predicate components of a natural language (NL) text segment onto their corresponding predicates within a knowledge base (KB).
Approach:	They propose a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems.
Outcome:	The proposed approach achieves an average performance gain of 17% on CLUTRR and relation linking in a KBQA system.

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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu

Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)

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.

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Yong Cao, Xianzhi Li, Huiwen Liu, Wen Dai, Shuai Chen, Bin Wang, Min Chen, Daniel Hershcovich

Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)

Challenge:	Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task.
Approach:	They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision.
Outcome:	The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods .

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Minhao Zhang, Ruoyu Zhang, Yanzeng Li, Lei Zou

TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data (2025.acl-long)

Challenge:	Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline .
Approach:	They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs.
Outcome:	Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient.

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Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng, Xiaxia Wang, Yuzhong Qu

Multimodality

Challenge:	Existing methods for semantic parsing rely on extensive manually annotated datasets and limited generalization capability to unseen examples.
Approach:	They propose a framework that generates high-relevance synthetic data without manual annotation . they generate queries for the queries and use them as demonstrations for in-context learning .
Outcome:	The proposed framework outperforms non-fine-tuned methods on KBQA datasets and shows superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings.

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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Yuhang Tian, Pan Yang, Dandan Song, Zhijing Wu, Hao Wang

Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (2022.coling-1)

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.

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Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu

ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)

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.

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Yu Gu, Yu Su

A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (2021.findings-emnlp)

Challenge:	Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time.
Approach:	They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking.
Outcome:	The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient.

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Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, Hong Chen

Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)

Challenge:	Existing knowledge bases (KBs) can explicitly facilitate the QA process.
Approach:	They propose a numerical reasoning model pretraining NumGNN and NumTransformer, guided by explicit self-supervision signals, to enhance numerical reasoning ability for IR-based KBQA models.
Outcome:	Extensive experiments on two KBQA benchmarks confirm the effectiveness of the proposed model.

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Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, Xu Sun

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Existing clarification datasets with limited annotated examples do not address ambiguous phenomena.
Approach:	They propose a dataset that allows users to ask clarification questions using open-domain examples.
Outcome:	The proposed model achieves better performance than strong baselines and provides new challenges.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Tengfei Feng, Liang He

Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)

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.

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Kangqi Luo, Fengli Lin, Xusheng Luo, Kenny Zhu

Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering (2021.acl-long)

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 .
Approach:	They propose to encode query structure into a uniform vector representation of a question and its semantic components into .
Outcome:	The proposed approach outperforms existing methods on complex questions while staying competitive on simple questions.

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Yunshi Lan, Jing Jiang

Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments (2023.acl-long)

Challenge:	a new method for conversational Knowledge Base Question Answering (KBQA) uses implied entities from the conversation history to answer questions.
Approach:	They propose to model the implied entities of conversational KBQA by applying a graph neural network to derive a probability distribution of focal entities for each question.
Outcome:	The proposed model captures transitions of focal entities and performs answer ranking on two datasets.

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Yu Gu, Xiang Deng, Yu Su

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

Challenge:	Existing language models lack grounding to real-world environments . a missing piece is the connection between LMs and the environment .
Approach:	They propose a generic framework for grounded language understanding that capitalizes on discriminative ability of LMs instead of their generative ability.
Outcome:	The proposed framework capitalizes on discriminative ability of LMs instead of their generative ability.

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Peng Yixing, Quan Wang, Licheng Zhang, Yi Liu, Zhendong Mao

SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases (2022.findings-emnlp)

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.

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Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-Suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L V Subramaniam

Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction (2023.emnlp-main)

Challenge:	Knowledge Base Question Answering (KBQA) systems have limited generalizability across knowledge bases and multiple reasoning types.
Approach:	They propose a modular approach for KBQA that is built on a framework adaptable to multiple knowledge bases and reasoning types.
Outcome:	The proposed approach is generalized across multiple knowledge bases and reasoning types.

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Nithish Kannen, Udit Sharma, Sumit Neelam, Dinesh Khandelwal, Shajith Ikbal, Hima Karanam, L Subramaniam

A New Concept of Knowledge based Question Answering (KBQA) System for Multi-hop Reasoning (2022.naacl-main)

Challenge:	Temporal question answering (QA) is a complex task that requires reasoning over facts asserting time intervals of events.
Approach:	They propose a temporal fact extraction technique that helps QA when it fails to retrieve temporal facts from the KB.
Outcome:	The proposed technique can extract temporal facts that failed to get retrieved from the KB without additional training cost.

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Yu Wang, V.srinivasan@samsung.com V.srinivasan@samsung.com, Hongxia Jin

Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)

Challenge:	Existing knowledge based question answering systems are trained based on labeled reasoning paths, which hinder their performance.
Approach:	They propose a KBQA system which leverages multiple reasoning paths’ information and only requires labeled answer as supervision.
Outcome:	The proposed system can leverage multiple reasoning paths’ information and only requires labeled answer as supervision.

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Yuanmeng Yan, Rumei Li, Sirui Wang, Hongzhi Zhang, Zan Daoguang, Fuzheng Zhang, Wei Wu, Weiran Xu

Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases (N19-1)

Challenge:	Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges.
Approach:	They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections.
Outcome:	Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete.

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Yu Chen, Lingfei Wu, Mohammed J. Zaki

Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)

Challenge:	Existing methods for knowledge base question answering ignore subtle inter-relationships between the question and the KB.
Approach:	They propose to model the two-way flow of interactions between questions and KBs using a bidirectional attentive memory network.
Outcome:	The proposed method outperforms existing methods on the WebQuestions benchmark and offers better interpretability compared to baselines.

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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu

RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions (2024.acl-long)

Challenge:	Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets.
Approach:	They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding.
Outcome:	The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia.

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Prayushi Faldu, Indrajit Bhattacharya, Mausam .

Robustness in NLP

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.

Few-shot In-context Learning on Knowledge Base Question Answering (2023.acl-long)

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Tianle Li, Xueguang Ma, Alex Zhuang, Yu Gu, Yu Su, Wenhu Chen

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)

Challenge:	KB-BINDER enables few-shot in-context learning over knowledge base questions . KBQA is a difficult problem due to the heterogeneity of knowledge bases .
Approach:	They propose a framework that enables few-shot in-context learning over KBQA tasks.
Outcome:	The proposed framework can outperform state-of-the-art models on GraphQA and MetaQA datasets.

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Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, Hong Chen

Multimodality

Challenge:	Existing retrieval methods for knowledge base question answering are either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs.
Approach:	They propose a subgraph retrieval framework that decouples the retrieval from the subsequent reasoning process and trains subgraphs for easier reasoning.
Outcome:	The proposed framework improves retrieval and QA performance over existing methods.

A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021.findings-emnlp)

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Linhai Zhang, Deyu Zhou, Chao Lin, Yulan He

Knowledge Representation

Challenge:	Existing methods treat multi-label learning problem as a single label . Existing approaches focus on measuring semantic similarity of questions and candidate relations .
Approach:	They propose to solve multi-hop relation detection problem by generating sequences of hops and labels.
Outcome:	The proposed method is effective in KBQA, despite the unknown number of labels and hops.

A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)

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Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue

A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)

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 .
Outcome:	The proposed framework achieves comparable or state-of-the-art performance on datasets with a different knowledge base.

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Deyu Zhou, Yanzheng Xiang, Linhai Zhang, Chenchen Ye, Qian-Wen Zhang, Yunbo Cao

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering (2022.acl-long)

Challenge:	Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths.
Approach:	They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths .
Outcome:	The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset.

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Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou, Caiming Xiong

KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)

Challenge:	Existing KBQA approaches struggle with generalization of unseen KB schema items . Rank-and-generate approach solves coverage issue with strong generalization .
Approach:	They propose a Rank-and-Generate approach for KBQA that uses a generation model to generalize to unseen KB schema items.
Outcome:	The proposed approach outperforms the prior state-of-the-art on GrailQA and WebQSP datasets.

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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang

Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation (2025.emnlp-main)

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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Shengxiang Gao, Jey Han Lau, Jianzhong Qi

HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering (2025.findings-acl)

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.

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Aziguli Wulamu, Lyu Zhengyu, Kaiyuan Gong, Yu Han, Zewen Wang, Zhihong Zhu, Bowen Xing

Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning (2024.acl-long)

Challenge:	Existing approaches struggle with mapping questions to precise logical forms . Existing frameworks struggle with complex mapping of questions to logical form .
Approach:	They propose a framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation.
Outcome:	The proposed framework outperforms supervised fine-tuning methods and training-free ones on large language models.

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Mayur Patidar, Riya Sawhney, Avinash Singh, Biswajit Chatterjee, Mausam ., Indrajit Bhattacharya

TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)

Challenge:	Existing Knowledge Base Question Answering (KBQA) architectures are expensive and time-consuming to deploy.
Approach:	They propose a KBQA architecture that performs KB-retrieval using multiple source-trained retrievers and re-ranks using an LLM.
Outcome:	The proposed architecture outperforms adaptations of SoTA KBQA models when training data is limited.

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Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin

Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)

Challenge:	KBQA is a challenging area for pre-trained language models due to its extensive space and complexity.
Approach:	They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors .
Outcome:	The proposed model outperforms existing models on GrailQA and WebQuestionsSP.

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Zhiqiang Zhang, Liqiang Wen, Wen Zhao

Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models (2024.acl-long)

Challenge:	Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process.
Approach:	They propose a framework that employs learned rules to guide the generation of logical forms.
Outcome:	The proposed method achieves competitive results on standard KBQA datasets.

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Guanming Xiong, Junwei Bao, Wen Zhao

Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions (2023.acl-long)

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.

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Mayur Patidar, Prayushi Faldu, Avinash Singh, Lovekesh Vig, Indrajit Bhattacharya, null Mausam

A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering (2022.emnlp-main)

Challenge:	missing facts, incomplete schema and limited scope lead to many questions being unanswerable.
Approach:	They propose to adapt a KBQA dataset with unanswerable questions to detect missing facts and incomplete schema.
Outcome:	The proposed model performs poorly even after adaptation for unanswerable questions.

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Minghui Xie, Chuzhan Hao, Peng Zhang

KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)

Challenge:	Existing methods for multi-hop reasoning in knowledge base question answering are coarse-grained and may bring information loss.
Approach:	They propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each hop in a more fine-grained way.
Outcome:	The proposed model achieves new state-of-the-art Hits@1 of 76.8% on WebQSP and is also effective when KB is incomplete.

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Ranran Bu, Jian Cao, Jianqi Gao, Shiyou Qian, Hongming Cai

MarkQA: A large scale KBQA dataset with numerical reasoning (2023.emnlp-main)

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.

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Xiang Huang, Sitao Cheng, Yuheng Bao, Shanshan Huang, Yuzhong Qu

Reasoning

Challenge:	Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning.
Approach:	They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning.
Outcome:	The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning.

Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction (2024.findings-acl)

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Albert Sawczyn, Katsiaryna Viarenich, Konrad Wojtasik, Aleksandra Domogała, Marcin Oleksy, Maciej Piasecki, Tomasz Kajdanowicz

From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base (2023.emnlp-main)

Challenge:	Existing KBQA datasets are outdated and inefficient in human labor, and assisting tools like Large Language Models (LLM) are not utilized to reduce the workload.
Approach:	They propose a semi-automated question answering task that uses structured knowledge graphs to answer extensive knowledge-intensive questions.
Outcome:	The proposed approach includes KBQA, MRC, and Information Retrieval tasks for low-resource languages.

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Wangzhen Guo, Linyin Luo, Hanjiang Lai, Jian Yin

KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)

Challenge:	Existing methods for parsing knowledge-base questions into executable logical forms have not been successful on complex KBQA.
Approach:	They propose a new semantic parser called KoPL to model the reasoning processes . they propose 'parse-execute-refine' paradigm to unlock reasoning ability .
Outcome:	The proposed parser performs better than the state-of-the-art on complex KBQA . the proposed parsed-execute-refine paradigm can model complex reasoning steps .

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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering (2025.acl-long)

Challenge:	Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals.
Approach:	They propose a self-supervised framework that enhances RAG systems through efficient model adaptation.
Outcome:	The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models.

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Shuai Wang, Yinan Yu

Knowledge Representation

Challenge:	Large language models suffer from factual inaccuracies in knowledge-intensive domains.
Approach:	They propose a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions and integrates a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step.
Outcome:	The proposed framework improves on four benchmark datasets and four LLMs.

SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions (2024.findings-emnlp)

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Shicheng Liu, Sina Semnani, Harold Triedman, Jialiang Xu, Isaac Zhao, Monica Lam

No Need for Large-Scale Search: Exploring Large Language Models in Complex Knowledge Base Question Answering (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have led to significant improvements in the Knowledge Base Question Answering task.
Approach:	They introduce an expert-annotated KBQA dataset from Wikidata’s “Request a Query” forum with 320 decontextualized question-SPARQL pairs.
Outcome:	The SPINACH dataset outperforms baselines on the QALD-7, QADL-9 Plus and QAL-10 datasets by 31.0%, 27.0% and 10.0% in F1 respectively.

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Shouhui Wang, Biao Qin

Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerability (2025.findings-acl)

Challenge:	Knowledge Base Question Answering (KBQA) systems are a key research area in the field of natural language processing and information retrieval (IR).
Approach:	They propose to use large language models to convert natural language questions to structured knowledge representations by using a three-step fine-tune strategy to implement the KBQA system.
Outcome:	The proposed method achieves state-of-the-art performance across three datasets with a 79.9% F1 score.

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Riya Sawhney, Samrat Yadav, Indrajit Bhattacharya, Mausam Mausam

Generating then Refining for Reliable Knowledge Base Question Answering (2026.acl-long)

Challenge:	Existing models for KBQA with unanswerable questions are inadequate for real-world applications.
Approach:	They propose a task of few-shot transfer for KBQA with unanswerable questions that extends FuSIC-KBQA to include feedback for unanswered questions.
Outcome:	The proposed model outperforms suitable adaptations of multiple LLM-based and supervised SoTA models on the task while establishing a new performance for answerable few-shot transfer as well.

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Jianqi Gao, Hang Yu, Jian Cao, Ranran Bu, Jinghua Tang, Nengjun Zhu, Yonggang Zhang

COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

Challenge:	Existing knowledge base question answering methods generate LFs that are non-executable due to semantic hallucination issue of large language models.
Approach:	They propose a "generate-verify-refine" framework for reliable LF generation . they propose ARI-KBQA to generate query paths based on hop-by-hop reasoning .
Outcome:	The proposed framework significantly improves model performance with a reduced search space . ARI-KBQA can generate LFs that are non-executable due to semantic hallucination issue .

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Boci Peng, Xiao Liu, Boren Hu, Yun Zhu, Xuanbo Fan, Yanwei Yue, Chunyu Yang, Yan Zhang

Multimodality

Challenge:	Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility.
Approach:	They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem.
Outcome:	The proposed framework captures the trade-off between information relevance and structural complexity.

Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models (2026.acl-long)

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Yinan Liu, Dongying Lin, Sigang Luo, Xiaochun Yang, Bin Wang