Papers by Can Ma
Plan-then-Seam: Towards Efficient Table-to-Text Generation (2023.findings-eacl)
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| Challenge: | Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. |
| Approach: | They propose a non-parallelelizable table-to-text model that produces outputs in parallel with one network. |
| Outcome: | The proposed model achieves 3.0 5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors. |
RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)
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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma
| Challenge: | Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation. |
| Approach: | They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation. |
| Outcome: | The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics. |
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)
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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Recent code large language models have demonstrated impressive performance on code-related tasks. |
| Approach: | They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations . |
| Outcome: | The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models . |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
| Approach: | They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |
Learning Autonomous Driving Tasks via Human Feedbacks with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing systems focus on making autonomous driving decisions without human interaction, but human-like decision-making is still an important factor in designing autonomous driving systems. |
| Approach: | They propose a framework leveraging Large Language Models for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback. |
| Outcome: | The proposed framework can match baseline extensively trained reinforcement learning models in driving scenarios and store optimal driving programming policy using Retrieval Augmented Generation (RAG). |
Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)
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| Challenge: | Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. |
| Approach: | They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input. |
| Outcome: | The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. |
Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation (2021.acl-long)
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| Challenge: | Experimental results show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering. |
| Approach: | They propose to build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph. |
| Outcome: | The proposed method outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering. |
Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)
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| Challenge: | Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation. |
| Approach: | They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline. |
| Outcome: | The proposed framework surpasses open-source RMs by an average of 8.2%. |
Graph-to-Text Generation with Dynamic Structure Pruning (2022.coling-1)
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| Challenge: | Recent studies show that explicitly modeling the input graph structure can significantly improve the performance. |
| Approach: | They propose a structure-aware cross-attention mechanism to re-encode the graph representation conditioning on the newly generated context at each decoding step. |
| Outcome: | The proposed model improves performance on two graph-to-text datasets with only minor increase on computational cost. |
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality (2023.acl-long)
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Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li
| Challenge: | Current datasets bias in the English language while leaving other languages underexplored. |
| Approach: | They propose a Chinese answer-to-sequence dataset with high quality and large scale . they propose encoding space for two hybrid knowledge resources to convert this task to a graph-totext problem. |
| Outcome: | The proposed method is effective in generating textual descriptions for the Chinese answer-to-sequence dataset. |
Free your mouse! Command Large Language Models to Generate Code to Format Word Documents (2024.emnlp-main)
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| Challenge: | Recent LLMs have significantly improved code generation, making it increasingly accessible to users. |
| Approach: | They propose an automatic document formatting method, Text-to-Format, driven by various prompting strategies and a high-quality dataset DocFormEval data. |
| Outcome: | The proposed method improves the efficiency and experience of users in formatting the document and improves document formatting task. |