Papers by Haidong Wang
WebNovelBench: Placing LLM Novelists on the Web Novel Distribution (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing benchmarks for long-form novel generation lack scale, diversity, or objective measures. |
| Approach: | They propose a framework that assesses long-form novel generation using an LLM-as-Judge approach. |
| Outcome: | The proposed framework differentiates between human-written masterpieces, popular web novels, and LLM-generated content. |
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that large language models (LLMs) can be effective for correcting factual inaccuracies but can still suffer from hallucinations. |
| Approach: | They propose a queue-based self-correction framework that addresses parameter bias during sequential model editing. |
| Outcome: | The proposed framework outperforms baseline models while maintaining competitive performance in single-turn editing. |
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. |
| Approach: | They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% . |
| Outcome: | The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction. |
FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models (2025.emnlp-main)
Copied to clipboard
Hengxing Cai, Jinhan Dong, Jingjun Tan, Jingcheng Deng, Sihang Li, Zhifeng Gao, Haidong Wang, Zicheng Su, Agachai Sumalee, Renxin Zhong
| Challenge: | Existing methods for vision-and-language navigation struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. |
| Approach: | They propose a framework for UAV vision-and-language navigation that integrates natural language instructions with visual observations to improve multimodal fusion and interpretability. |
| Outcome: | The proposed framework achieves state-of-the-art performance across all scenarios, with a 9.22% higher success rate than the strongest baseline in unseen environments. |