Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yongqi Leng, Renren Jin, Yue Chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The widespread deployment of large language models (LLMs) across various domains has made their safety a critical priority.Inspired by think-tank decision-making philosophy, we propose DiplomacyAgent, an LLM-based multiagent system for diplomatic position analysis.With DiplomacyAgent, we are able to systematically assess how LLMs balance "interests" against "ethical principles" when addressing various international events, hence understanding the safety implications of LLMs in diplomacy.Specifically, this will help to assess the consistency of LLM stance with widely recognized ethical standards, as well as the potential risks or ideological biases that may arise.Through integrated quantitative metrics, our research uncovers unexpected decision-making patterns in LLM responses to sensitive issues including human rights protection, environmental sustainability, regional conflicts, etc.It discloses that LLMs could exhibit a strong bias towards interests, leading to unsafe decisions that violate ethical and moral principles.Our experiment results suggest that deploying LLMs in high-stakes domains, particularly in the formulation of diplomatic policies, necessitates a comprehensive assessment of potential ethical and social implications, as well as the implementation of stringent safety protocols.

Large language models (LLMs) based on the Transformer architecture usually have their context length limited due to the high training cost.Recent advancements extend the context window by adjusting the scaling factors of RoPE and fine-tuning.However, suboptimal initialization of these factors results in increased fine-tuning costs and reduced performance at target length.To address these challenges, we propose a novel RoPE-based fine-tuning framework that diverges from conventional scaling factors search.Specifically, we present a Divide-and-Conquer Incremental Search (DCIS) algorithm that strategically determines the better scaling factors.Further finetuning with the identified scaling factors effectively extends the context window of LLMs.Empirical results demonstrate that our methodology not only mitigates performance decay at extended target lengths but also allows the model to fine-tune on short contexts and generalize to long contexts, thereby reducing the cost of fine-tuning.The scaling factors obtained through DCIS can even perform effectively without fine-tuning.Further analysis of the search space reveals that DCIS achieves twice the search efficiency compared to other methods.We also examine the impact of the non-strictly increasing scaling factors utilized in DCIS and evaluate the general capabilities of LLMs across various context lengths.