Papers by Kexuan Sun

4 papers
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering (2024.lrec-main)

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Challenge: Existing approaches to QA over textual data are based on a "retrieve-then-generate" pipeline.
Approach: They propose a "triple-level" labeling strategy that infers fine-grained labels and trains a re-ranker to improve relevance of retrieved triples.
Outcome: The proposed pipeline improves on prior KGQA systems by 5.56% Exact Match.
Table-based Fact Verification With Salience-aware Learning (2021.findings-emnlp)

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Challenge: Existing methods for fact verification use tabular data with tokens, but training requires labeled training data.
Approach: They propose a system that identifies token-level salience in the statement with probing-based saliency estimation.
Outcome: The proposed system improves on TabFact benchmark by replacing non-salient terms with tokens.
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning (2020.emnlp-main)

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Challenge: Walk-based models have shown their advantages in knowledge graph reasoning but are limited by their representations and generalizability.
Approach: They propose a walk-based model that leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk- based agents.
Outcome: Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data (2026.acl-long)

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Challenge: Existing datasets are small, domain-limited, and lack diversity, constraining LLM progress.
Approach: They propose a knowledge Graph retrieval tool that can translate natural language questions into structured queries.
Outcome: Extensive experiments show that CypherSmith achieves state-of-the-art performance.

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