Papers with KQA Pro
Handling Ontology Gaps in Semantic Parsing (2024.starsem-1)
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| Challenge: | Existing methods to detect hallucinations in closed-ontology models are limited by ontology gaps. |
| Approach: | They propose a framework for stimulating and analyzing NSP model hallucinations . they propose 'hallucination simulation framework' to detect hallucinosities in presence of ontology gaps . |
| Outcome: | The proposed framework improves the F1-Score and the IQ Pro benchmark datasets. |
Aligning Complex Knowledge Graph Question Answering as Knowledge-Aware Constrained Code Generation (2025.coling-main)
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| Challenge: | Existing frameworks that generate LF using Large Language Models (LLMs) in a few-shot setting are limited due to little exposure to the LF during pre-training. |
| Approach: | They propose a framework that aligns the LF generation as code generation that incorporates LF-specific constraints. |
| Outcome: | The proposed framework surpasses all few-shot baselines on KQA Pro by 21%. |
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)
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| Challenge: | Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages. |
| Approach: | They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. |
| Outcome: | The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
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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 . |
SymKGQA: Few-Shot Knowledge Graph Question Answering via Symbolic Program Generation and Execution (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models have led to low-level LFs that are limited to the knowledge of underlying LLM about the LF. |
| Approach: | They propose a framework that generates a symbolic LF in a few-shot setting using Large Language Models. |
| Outcome: | The proposed framework outperforms all other few-shot and many fully-supervised KGQA approaches. |
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. |
A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering (2025.findings-emnlp)
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| Challenge: | Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) KGQA methods based on LLMs are limited in their ability to model KG structure without additional data. |
| Approach: | They propose a KGQA framework that can operate in a zero-shot setting . they propose NS-KGQA to use neural KG embeddings to model KG structure . |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot baselines by 26%. |
PO-KGQA: Preference Optimization for Low-Resource Complex Knowledge Graph Question Answering (2026.findings-acl)
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| Challenge: | Existing low-resource in-context learning-based knowledge graph question answering methods rely heavily on large language models to convert natural language questions into logical forms. |
| Approach: | They propose a low-resource in-context learning-based knowledge graph question answering (KGQA) that uses large language models to convert a natural language question into its corresponding logical form. |
| Outcome: | The proposed method outperforms other methods on complex benchmarks by approximately 9% (avg). |