Papers by Jinjie Ni
SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026.findings-acl)
Copied to clipboard
| Challenge: | SynthRL synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. |
| Approach: | They propose a scalable and guaranteed pipeline for automatic data scaling in reasoning-oriented RL training. |
| Outcome: | The proposed pipeline synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. |
ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)
Copied to clipboard
Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou
| Challenge: | Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. |
| Approach: | They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks. |
| Outcome: | The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy. |
Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization. |
| Approach: | They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset. |
| Outcome: | The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters. |