Papers by Jiahui Liang
LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts (2026.eacl-long)
Copied to clipboard
| Challenge: | LM-LEXICON is a definition modeling approach that integrates data clustering, semantic expert learning, and model merging. |
| Approach: | They propose a definition modeling approach that integrates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. |
| Outcome: | The proposed model outperforms existing methods on five widely used benchmarks and achieves a BLEU score of 7%. |
Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Exploration (2026.acl-long)
Copied to clipboard
| Challenge: | Existing Large Language Models suffer from "Reasoning Collapse" on mathematical reasoning tasks where stochastic sampling produces lexical variations of the same erroneous logic rather than genuine semantic exploration. |
| Approach: | They propose a geometric inference framework that uses a spectral orthogonal probe to introduce semantically heterogeneous reasoning signals into the teacher's orthogonale complement of its dominant subspace. |
| Outcome: | The proposed framework improves accuracy and sampling efficiency over baseline methods on logic and code generation benchmarks. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
Copied to clipboard
Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |