Papers by Yiwei Liang
GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing work only encodes entity types and textual context within individual instances, which limits the performance of sentence-level relation extraction (RE). |
| Approach: | They propose a module that aggregates the features from sentences to learn global representations of properties and augments local features within individual sentences. |
| Outcome: | The proposed module can learn global representations of properties from sentences and augment local features within individual sentences. |
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)
Copied to clipboard
Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen
| Challenge: | Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios. |
| Approach: | They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance. |
| Outcome: | The proposed framework improves model capabilities across all domains and scales. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
Copied to clipboard
Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
Primacy Effect of ChatGPT (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing machine learning models may lead to poor performance in discriminative natural language understanding tasks. |
| Approach: | They propose to use ChatGPT to query large amounts of human-written text to find the answer to a question. |
| Outcome: | The proposed model has a high chance to select labels at earlier positions as the answer. |
CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval (2026.findings-acl)
Copied to clipboard
Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun
| Challenge: | Existing approaches to search for images using single-modality are limited by representation space fragmentation. |
| Approach: | They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images . |
| Outcome: | The proposed framework achieves efficient query-target alignment through synergistic components. |
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)
Copied to clipboard
Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi
| Challenge: | Existing studies rely on entity information for sentence-level relation extraction (RE) but this can leak superficial and spurious clues of relations. |
| Approach: | They propose to use entity mentions to extract relations from textual context . they use a causal graph to model dependencies between variables in RE models . |
| Outcome: | The proposed method yields significant gains on both effectiveness and generalization for RE. |
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing LLMs are delicate and elusive in prompt words and styles. |
| Approach: | They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning . |
| Outcome: | The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality. |
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer (2021.naacl-main)
Copied to clipboard
Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods for text style transfer focus on individual high-level semantic changes but do not offer fine-grained control of sentence structure, emphasis, and content. |
| Approach: | They propose a large-scale text style transfer benchmark with 21 fine-grained stylistic changes across atomic lexical, syntactic, semantic, and thematic transfers. |
| Outcome: | The proposed method allows modeling fine-grained changes as building blocks for more complex, high-level transfers. |
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. |
| Approach: | They propose a few-shot human-in-the-loop training algorithm that allows distribution control for text generation via human feedback. |
| Outcome: | The proposed algorithm achieves state-of-the-art results on single topic/attribute and quantified distribution control compared to previous works. |