Papers with CARL
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)
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| Challenge: | Recent work has shown that models can be easily fooled by intentionally designed adversarial examples. |
| Approach: | They propose two efficient approaches for generating adversarial perturbations on embeddings and propose two new approaches to help model learn adversarials more efficiently. |
| Outcome: | The proposed approaches outperform strong baselines on various text classification datasets and the model's performance drops less under adversarial attack. |
Compete to Complete: Co-opetition Adversarial Learning for Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to reduce hallucination in large language models lack a robust mechanism for generating a generative model. |
| Approach: | They propose a framework that formulates retriever–generator training in RAG as a minimax game. |
| Outcome: | The proposed framework improves retrieval-augmented generation performance on seven benchmark datasets. |
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)
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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
| Challenge: | Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints. |
| Approach: | They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs. |
| Outcome: | The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications. |
DynaQuest: A Dynamic Question Answering Dataset Reflecting Real-World Knowledge Updates (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are typically trained on static datasets, preventing them from integrating real-time updates. |
| Approach: | They propose a dynamic question-answer answering dataset reflecting real-world knowledge updates that are automatically compared between Wikipedia versions and generating question-anchor pairs based on these updates. |
| Outcome: | The proposed framework improves LLMs' performance on time-sensitive question answering by maintaining a dynamic knowledge updating process. |