Papers by Jiaqian Wang
From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning. |
| Approach: | They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs. |
| Outcome: | Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains. |
STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment (2025.emnlp-main)
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| Challenge: | Existing methods for incontext learning often overlook structural alignment, leading to poor generalization and suboptimal performance. |
| Approach: | They propose a two-stage exemplar selection strategy that achieves a strong balance between efficiency, generalizability and performance. |
| Outcome: | The proposed method outperforms baselines on semantic parsing tasks on four benchmarks. |