Papers by Yanbin Hao
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)
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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu
| Challenge: | closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges . |
| Approach: | They propose a framework that leverages collective intelligence from all large language models to evaluate each other. |
| Outcome: | a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost. |
Unified Contextual Query Rewriting (2023.acl-industry)
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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life. |
| Approach: | They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance . |
| Outcome: | The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks. |
Cross-sentence Pre-trained Model for Interactive QA matching (2020.lrec-1)
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| Challenge: | Existing methods for semantic matching do not examine each sentence individually, but consider syntactic context inside a sentence. |
| Approach: | They propose a semantic matching model that takes a cross-sentence context-aware architecture and incorporates a quantity of context information jump to facilitate attention weight formulation. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Yahoo! community question dataset and the TREC library. |
Mixture of Multimodal Adapters for Sentiment Analysis (2025.naacl-long)
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| Challenge: | Pre-trained language models (PLMs) have been used for text sentiment analysis but sentiment is hidden in other modalities. |
| Approach: | They propose to fuse emotions from different data to analyze sentiments . they use compression parameter for each expert to reduce training burden . |
| Outcome: | The proposed method achieves state-of-the-art with a tiny trainable parameter count compared to current methods . emotions hidden in body movements or vocal timbres eclipse traditional methods compared with text sentiment analysis . |
Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)
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| Challenge: | Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent. |
| Approach: | They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites. |
| Outcome: | The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites. |