Papers by Colin Zhang
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
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| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
| Approach: | They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB) |
| Outcome: | The proposed method improves state-of-the-art for OpenIE extractions and boosts performance on OpenIE from semi-structured data. |
Extracting Shopping Interest-Related Product Types from the Web (2023.findings-acl)
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| Challenge: | Existing e-commerce products are limited in their ability to assist customers in interest-oriented shopping. |
| Approach: | They propose to extract PTs from Web pages containing hand-crafted PT recommendations for SIs . they propose to use tree-transformer encoders for node classification to improve inter-node dependency modeling . |
| Outcome: | The proposed model outperforms the best baseline model by 2.37 F1 points on a WebPT dataset. |
Evaluating the Factual Consistency of Large Language Models Through News Summarization (2023.findings-acl)
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| Challenge: | Existing LLMs generally assign a higher score to factually consistent summaries than to factualally inconsistent summary. |
| Approach: | They propose a benchmark to measure whether large language models prefer factually consistent continuations of inputs. |
| Outcome: | The proposed benchmark compares the scores an LLM assigns to a factually consistent versus a inconsistent summary for an input news article. |
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) have prioritized expanding the context window from which they can incorporate more information. |
| Approach: | They propose a data augmentation strategy to enable large language models to gain long-context capabilities without the need to modify existing data mixture. |
| Outcome: | The proposed model outperforms existing models on 20 billion tokens and achieves 75% and 84.5% accuracy on RULER at 128K context length. |