Papers by Yanna Ma
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem (2022.findings-emnlp)
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| Challenge: | Existing methods for generating complex semantics and diverse equations are limited by a fixed view. |
| Approach: | They propose a multi-view consistent contrastive learning approach that decouples human reasoning into two independent but consistent views. |
| Outcome: | The proposed approach significantly outperforms existing baselines on complex problems on multiple languages. |
An Expression Tree Decoding Strategy for Mathematical Equation Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to generate mathematical equations from natural language ignore parallel or dependent relations between math expressions. |
| Approach: | They propose to integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. |
| Outcome: | The proposed method outperforms baseline methods for generating mathematical equations from natural language. |
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection (2025.acl-long)
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| Challenge: | Existing work has been using decoding-free candidate selection methods to obtain candidate probability from initial output logits over vocabulary. |
| Approach: | They propose to evaluate a set of tasks using decoding-free candidate selection methods on a comprehensive set of questions. |
| Outcome: | The proposed methods are evaluated on a set of tasks including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with 10k+ options. |
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model (2024.emnlp-main)
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Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang
| Challenge: | Using large language models, large multimodal models struggle with basic tasks like reading time from a clock and planning a route using a road map. |
| Approach: | They propose a multimodal self-instruct that synthesizes massive abstract images and visual reasoning instructions. |
| Outcome: | The proposed model synthesizes 11,193 abstract images and reasoning instructions across eight visual scenarios. |