Papers by Shuhan Guo
Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. |
| Approach: | They propose a method to iteratively refine task descriptions and metamorphosis on algorithms to generate more effective solutions. |
| Outcome: | Experimental results show that Nested-Refinement Metamorphosis outperforms state-of-the-art approaches in performance and efficiency. |
MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Multimodal Models (LMMs) have raised concerns about model toxicity. |
| Approach: | They propose a model to measure the toxicity gap between models and their hard level to determine whether they can handle dual-implicit toxicity. |
| Outcome: | The proposed model can handle dual-implicit toxicity effectively on 13 prominent LMMs, but its performance drops significantly in hard level. |