Papers by Xiaolong Jia
RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)
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| Challenge: | despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions. |
| Approach: | They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation. |
| Outcome: | The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency. |
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations. |
| Approach: | They propose a framework that reformulates retrieval and generation as constrained optimization and path planning. |
| Outcome: | The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations. |
Foot-In-The-Door: A Multi-turn Jailbreak for LLMs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into real-world applications, requiring a high level of safety and alignment. |
| Approach: | They propose a multi-turn jailbreak method that leverages foot-in-the-door principles to escalate malicious intent of user queries through intermediate bridge prompts and aligns the model’s response by itself to induce toxic responses. |
| Outcome: | The proposed method achieves an average attack success rate of 94% across seven widely used models outperforming existing state-of-the-art methods. |