Papers by Huaping Liu
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
Copied to clipboard
Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |
ProcWorld: Benchmarking Large Model Planning in Reachability-Constrained Environments (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing benchmarks for embodied spatial reasoning and long-term planning are non-trivial due to the combinatorial complexity of long-horizon abstract reasoning. |
| Approach: | They propose a large-scale benchmark for partially observable embodied spatial reasoning and long-term planning with large language models and vision language models. |
| Outcome: | The proposed model performs better in 16 task types, 5,000 rooms, and over 10 million evaluation trajectories with diverse data distribution. |
Towards Objectively Benchmarking Social Intelligence of Language Agents at the Action Level (2024.findings-acl)
Copied to clipboard
| Challenge: | evaluative findings highlight that the STSS benchmark is challenging for state-of-the-art language agents. |
| Approach: | They propose a social task in sandbox simulation benchmark that assesses language agents objectively at the action level by scrutinizing goal achievements within the multi-agent simulation. |
| Outcome: | The proposed social task-in-sandbox simulation is a language-level benchmark . the proposed benchmark effectively discriminates between distinct language agents . |