Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)
Copied to clipboard
| 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. |
Similar Papers
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)
Copied to clipboard
| 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. |
Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)
Copied to clipboard
| 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. |
Complex Question Decomposition for Semantic Parsing (P19-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed method improves on a large scale complex question semantic parsing dataset. |
CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)
Copied to clipboard
Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang
| 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. |
Generating Complex Question Decompositions in the Face of Distribution Shifts (2025.naacl-long)
Copied to clipboard
| Challenge: | Question decomposition has been found to improve large language models’ (LLMs) performance on complex question answering (QA) however, performance on the task remains dominated by supervised approaches, suggesting room for making LLMs better decomposers. |
| Approach: | They propose to generate synthetic decomposition data with only five annotated examples by extending recent advances in using LLM-as-judge and for reranking in novel ways. |
| Outcome: | The proposed approach generates synthetic decomposition data with only five examples over two benchmark datasets. |
Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing Knowledge-based Question Answering methods use a query graph to find the answer to a question. |
| Approach: | They propose a method that starts with the entire knowledge base and gradually shrinks it to the desired query graph. |
| Outcome: | Experimental results show that the proposed method achieves state-of-the-art performance on ComplexWebQuestion dataset. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
Copied to clipboard
Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
| 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 . |
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models (2024.acl-long)
Copied to clipboard
| 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. |
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)
Copied to clipboard
Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue
| 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. |
The Web as a Knowledge-Base for Answering Complex Questions (N18-1)
Copied to clipboard
| Challenge: | Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. |
| Approach: | They propose to decompose complex questions into a sequence of simple questions and compute the final answer from the sequence of answers. |
| Outcome: | The proposed framework improves performance from 20.8 precision@1 to 27.5 precision@1. |