Papers by Kaiqi Yang
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs). |
| Approach: | They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs. |
| Outcome: | The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting. |
Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from. |
| Approach: | They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task. |
| Outcome: | The proposed model performs well in three realistic settings and a novel social prediction task. |
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)
Copied to clipboard
Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun
| Challenge: | Existing approaches to safety alignment of large language models rely on costly manual annotations or human review. |
| Approach: | They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation. |
| Outcome: | The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability. |
SAMem: State-Aware Memory as a Fine-Grained Memory for LLM Agents in Decision-Making (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing experiential memory approaches rely on task-level memory, but this lacks the situational alignment required for complex multi-step decision-making. |
| Approach: | They propose a new fine-grained memory paradigm that aligns memory retrieval with the current state instead of storing and reusing globally shared experiences. |
| Outcome: | Experiments on complex decision-making benchmarks show that the proposed state-aware memory outperforms existing experiential memory approaches and significantly improves task-solving efficiency. |
CDAˆ2: Counterfactual Diffusion Augmentation for Cross-Domain Adaptation in Low-Resource Sentiment Analysis (2025.coling-main)
Copied to clipboard
| Challenge: | Domain adaptation is widely employed in cross-domain sentiment analysis, but concerns have been raised regarding their robustness and sensitivity to data distribution shift. |
| Approach: | They propose a framework CDA2 for cross-domain adaptation in low-resource sentiment analysis which employs counterfactual diffusion augmentation. |
| Outcome: | The proposed framework generates high-quality counterfactual target samples and achieves state-of-the-art performance on benchmark datasets. |