Papers by Zhang Huaping
ConMA : Confidence-Guided Kernel Sampling with Multi-Stage Aggregation for LLM Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to test-time scaling rely on external verifiers and one-shot independent sampling. |
| Approach: | They propose a test-time scaling framework that reallocates a fixed inference budget into iterative sample–filter–diversify–select cycles. |
| Outcome: | ConMA outperforms baselines on multiple benchmarks while converging early with only 18 samples on average, substantially reducing inference cost. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
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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)
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| 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. |
PsyAttention: Psychological Attention Model for Personality Detection (2023.findings-emnlp)
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| Challenge: | Personality detection has incorporated psychological features from different personality models, such as the BigFive and MBTI. |
| Approach: | They propose to use psychological models to encode personality features to reduce their number by 85%. |
| Outcome: | The proposed model outperforms state-of-the-art methods on the BigFive and MBTI models and achieves average accuracy of 65.66% and 86.30%, respectively. |
Context Length Extension via Generalized Extrapolation Scale (2024.findings-acl)
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| Challenge: | Existing work on extrapolating positional embedding (RoPE) has limited results in the application of long context language models. |
| Approach: | They propose a set of parameterized extrapolation functions applied to each layer and attention head to adaptively adjust its extrapolations scales. |
| Outcome: | The proposed model achieves stable extrapolation on 64k contexts by training on 16k length text. |