Papers by Zhuoqun Hao
Towards Rationality in Language and Multimodal Agents: A Survey (2025.naacl-long)
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Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Yuan Yuan, Zhuoqun Hao, Xinyi Bai, Weijie J Su, Camillo Jose Taylor, Tanwi Mallick
| Challenge: | despite advances in language and multimodal agents, large language models lack rationality . despite their progress, large-scale models lack real-world grounding and feedback mechanisms . |
| Approach: | They propose to build more rational language and multimodal agents . they also examine what criteria define rationality in intelligent systems . |
| Outcome: | This paper assesses the state-of-the-art in language and multimodal agents . it also outlines open challenges and future research directions . |
ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations (2025.findings-emnlp)
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Bowen Jiang, Yuan Yuan, Xinyi Bai, Zhuoqun Hao, Alyson Yin, Yaojie Hu, Wenyu Liao, Lyle Ungar, Camillo Jose Taylor
| Challenge: | a new method for visual text rendering requires glyph annotations to be obtained . |
| Approach: | They propose a model that integrates diffusion with a text segmentation model to achieve multilingual text rendering using just raw images without font label annotations. |
| Outcome: | The proposed model can achieve font-controllable multilingual text rendering without label annotations. |
Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have push NLP into a new era, moving away from traditional task-specific pre-train finetuning paradigm. |
| Approach: | They provide a comprehensive analysis of declarative and procedural knowledge for large language models and evaluate their effectiveness. |
| Outcome: | The proposed model can perform better with both kinds of knowledge, but at different speeds. |
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners (2024.emnlp-main)
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Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie Su, Camillo Taylor, Dan Roth
| Challenge: | a new hypothesis-testing framework is developed to assess whether large language models possess genuine reasoning abilities or primarily depend on token bias. |
| Approach: | They propose a framework to assess whether large language models have genuine reasoning abilities or primarily depend on token bias. |
| Outcome: | The proposed framework outlines a list of hypotheses where token biases are readily identifiable . the results suggest that most LLMs still struggle with logical reasoning . |