Papers by Shao-Hua Sun
Adaptive Helpfulness–Harmlessness Alignment with Preference Vectors (2026.eacl-long)
Copied to clipboard
Ren-Wei Liang, Chin Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun
| Challenge: | Existing approaches to balancing helpfulness and harmlessness suffer from performance conflicts, limited controllability, and poor extendability. |
| Approach: | They propose a framework that allows users to control their own preferences and dynamically merge them at test time. |
| Outcome: | The proposed framework improves helpfulness without conservatism and smooth control over preference trade-offs. |
Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations (2026.eacl-long)
Copied to clipboard
| Challenge: | Creativity measures that distinguish creativity in one domain fail in others, and different metrics disagree on the same data points. |
| Approach: | They examine, analyze, and compare four representative creativity measures across the diverse creative domains, including creative writing, unconventional problem-solving, and research ideation. |
| Outcome: | The measures of creativity across creative domains are compared using a set of human-aligned examples and lack consistency across domains and metrics. |
Location-Aware Visual Question Generation with Lightweight Models (2023.emnlp-main)
Copied to clipboard
Nicholas Suwono, Justin Chen, Tun Hung, Ting-Hao Huang, I-Bin Liao, Yung-Hui Li, Lun-Wei Ku, Shao-Hua Sun
| Challenge: | a novel task aims to generate engaging questions from location-aware information . a lightweight model can be used to generate such questions . |
| Approach: | They propose a task to generate engaging questions from location-aware data . they represent location-based information with surrounding images and a GPS coordinate . |
| Outcome: | The proposed method outperforms baselines regarding human evaluation and evaluation metrics. |
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation (2026.eacl-long)
Copied to clipboard
| Challenge: | Multi-LLM systems enhance creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. |
| Approach: | They propose a training-free framework that captures the benefits of multi-LLM collaboration by extracting and blending multiple distinct persona vectors directly in the model’s activation space. |
| Outcome: | The proposed framework surpasses model prompting and traditional multi-LLM approaches while significantly reducing inference time and computational costs. |