Papers by Jiapeng Wu
Factual Error Correction for Abstractive Summarization Models (2020.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization are unable to ensure factual consistency of generated summaries. |
| Approach: | They propose a post-editing corrector module to identify and correct factual errors in generated summaries. |
| Outcome: | The proposed model outperforms existing models on CNN/DailyMail dataset on factual consistency evaluation. |
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (2020.emnlp-main)
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| Challenge: | Existing methods for static knowledge graphs do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. |
| Approach: | They propose a framework to leverage time-dependent temporal information to infer missing facts in temporal knowledge graphs. |
| Outcome: | The proposed framework achieves 10.7% improvement in Hits@10 across three standard benchmarks. |
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation (2025.naacl-long)
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Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh
| Challenge: | Recent advances in text-to-SQL generation rely on large closed-source models that present challenges in accessibility, privacy, and latency. |
| Approach: | They propose to use open-source text-to-SQL models to critique SQL queries . their method evaluates multiple outputs simultaneously and is competitive with larger models . |
| Outcome: | The proposed method achieves state-of-the-art performance compared to open-source models while remaining competitive with larger models at a much lower cost. |