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