Papers by Kaijian Zou
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison (2023.emnlp-main)
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| Challenge: | a recent study shows that media influence opinion via the inclusion or omission of partisan events. |
| Approach: | They develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. |
| Outcome: | The proposed framework validates the existence of partisan event selection and detects partisan events and article ideology better than baselines. |
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting (2023.findings-emnlp)
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| Challenge: | Prior work in NLP has only studied media bias via linguistic style and word usage. |
| Approach: | They annotate a dataset containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. |
| Outcome: | The proposed dataset contains 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. |
On Many-Shot In-Context Learning for Long-Context Evaluation (2025.acl-long)
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| Challenge: | Existing benchmarks primarily evaluate long-context language models' retrieval capabilities. |
| Approach: | They propose a benchmark to evaluate long-context language models' retrieval capabilities by using MANYICLBENCH. |
| Outcome: | The proposed model performs better with additional demonstrations than translation and reasoning tasks. |
SYNC: A Synthetic Long-Context Understanding Benchmark for Controlled Comparisons of Model Capabilities (2025.emnlp-main)
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| Challenge: | Existing synthetic tasks target narrow skill sets, limiting their ability to comprehensively assess model capabilities. |
| Approach: | They propose a new evaluation suite of synthetic tasks spanning domains including graph understanding and translation that test a wide range of capabilities. |
| Outcome: | The evaluation suite of synthetic tasks spanning domains including graph understanding and translation shows that the tasks perform significantly better on more challenging tasks. |