Papers by Jiarui Yu
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues (2021.naacl-main)
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Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu
| Challenge: | Traditionally, anaphora resolution and ellipses resolution are limited in dialogues . despite rapid progress in dialogue systems, several difficulties remain . |
| Approach: | They propose a joint learning framework for modeling coreference resolution and query rewriting for complex, multi-turn dialogues. |
| Outcome: | The proposed model outperforms the state-of-the-art model on a rewritten dialogue dataset. |
Can Large Language Models Understand Context? (2024.findings-eacl)
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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng
| Challenge: | Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features. |
| Approach: | They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation. |
| Outcome: | The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models. |
SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness (2024.emnlp-main)
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Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri, Hyosang Ahn, Sreya Muppalla, Kai-Wei Chang, Wei Wang, Nanyun Peng
| Challenge: | Prior studies focused on English posts to provide early warnings for epidemic prediction, but these work focused on non-English posts. |
| Approach: | They propose a multilingual event extraction framework for extracting epidemic event information for any disease and language using 5.1K tweets in four languages. |
| Outcome: | The proposed framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) and aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring. |
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants (2023.emnlp-industry)
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Leon Zhang, Jiarui Lu, Joel Ruben Antony Moniz, Aditya Kulkarni, Dhivya Piraviperumal, Tien Dung Tran, Nick Tzou, Hong Yu
| Challenge: | Existing training datasets for steering use cases are limited due to the cold-start problem. |
| Approach: | They propose a steering detection model that predicts whether a follow-up turn is a user’s attempt to steer the previous command. |
| Outcome: | The proposed model outperforms existing models on human-graded evaluation sets and shows that it can identify steering intent with over 95% accuracy. |
Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (2026.acl-long)
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Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, Jiawei Chen
| Challenge: | Large Language Models (LLMs) have remarkable reasoning capabilities in complex tasks such as mathematics and coding. |
| Approach: | They propose an entropy-modulation method that adaptively reweighs tokens based on theoretically-estimated entropic variations. |
| Outcome: | The proposed method outperforms state-of-the-art methods in six mathematical reasoning and three coding benchmarks. |
LEPO: Latent Reasoning Policy Optimization for Large Language Models (2026.findings-acl)
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| Challenge: | Existing latent reasoning methods that use chain of thought (CoT) are limited to selecting one discrete token at each reasoning step, which potentially induces information loss. |
| Approach: | They propose a framework that injects controllable stochasticity into latent reasoning via Gumbel-Softmax, restoring LLMs' exploratory capacity and enhancing their compatibility with Reinforcement Learning (RL). |
| Outcome: | The proposed framework preserves richer information for more comprehensive reasoning and is compatible with Reinforcement Learning (RL). |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |