Papers by Yuanyuan Ding
ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)
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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen
| Challenge: | Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems. |
| Approach: | They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision. |
| Outcome: | The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks. |
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (D18-1)
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| Challenge: | Existing rule-based question generation models rely on one or two sentences as input, while long text has posed challenges for sequence to sequence neural models. |
| Approach: | They propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. |
| Outcome: | The proposed model outperforms existing models with sentence-level or paragraph-level inputs pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4). |